Relational Dynamic Bayesian Networks: a report. Cristina Manfredotti

Size: px
Start display at page:

Download "Relational Dynamic Bayesian Networks: a report. Cristina Manfredotti"

Transcription

1 Relational Dynamic Bayesian Networks: a report Cristina Manfredotti Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.) Università degli Studi Milano-Bicocca Bayesian Networks: C T T Encode the joint probability distribution of a set of variables, as a Direct Acyclic Graph A Direct Acyclic Graph which nodes are conditionally independent of its non-descendent given its parents B T T P(D C,B) D T P(E D) C E D D T A P(C) T.90 P( D) P(B A).05 B A P(A) 0.01 P(A,B,C,D,E,) = = P(A)P(B A)P(C)P(D C,B)P(E D)P( D) = P(Z i Pa(Z i )) Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 2

2 The alarm example(1) I'm at work, neighbor John calls to say my alarm is ringing, but neighbor Mary doesn't call. Sometimes it's set off by minor earthquakes. Is there a burglary? Variables: BurglarEnter, EarthquakeAppens, AlarmRings, JohnCalls, MaryCalls Network topology reflects "causal" knowledge: A burglar can set the alarm off An earthquake can set the alarm off The alarm can cause Mary to call The alarm can cause John to call from Russel&Norvig Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 3 The alarm example(2) Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 4

3 Bayesian Networks Each node is a variable: Two different nodes in the network This is why we have such structure: Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 5 Bayesian Networks If we should have 4 neighbors? We have to construct a graph with 2 more knods. Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 6

4 A large BN Thanks to Mark Chavira Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 7 Relational Domain Objects: groups of attributes which belong together (tables of a database), c.f. a structure in a programming language e.g.: Object Relational Domain contains a set of objects with relations and/or predicates between them e.g.: Relation neighbor alarm burglar (honer of an house) neighbor s attributes: his capacity of hearing, his attention,... alarm s attributes: its volume, its sensibility,... e.g.: Predicate tocall (the honer of the house) tohear (the alarm) toring Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 8

5 The alarm Relational Domain: Burglary Alarm Volume Sensibility ToRing... Listening Neighbor DegOfDef NoiseAround Teleph... Calling Honer DegOfBelieve Teleph... Red words: predicates, that concern only the object itself Dashed arrows: relation between an object and an attribute of the object (or a predicate) Green arrows: dependence between two attributes Continouse arrows: relations between two objects Bold black words: objects names Black words: objects attributes (caracteristic of the variables, they make an instanciation of each object different by each other). Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 9 Relational Bayesian Networks difficult definition Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 10

6 Relational Bayesian Network Syntax RBN: a set of nodes, one per variable predicate/relation/attribute a directed, acyclic graph a conditional distribution for each node given its parents, this distribution must take into account the actual complexity of the nodes! Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 11 Alarm RBN: Earthquacke Neigh.DegOfDef Alarm.Volume Neigh.NoiseAround I relationated only that part of the graph, I could make the same for each knodes of the BN NeighborCalls Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 12

7 Conditional Probability Distribution/Table The CPTs will take into account the values of each attributes or each variable in the system (i.e. for each actor playing a role in the represented world) an object will be instantiated, the conditional probability of each variable will be the same but they will depend by the particular instantiation of their attributes. NOT ONLY BY THE ACT THAT THE ALARM HAS RANG E.g.: P(NeighCall Neigh.DegOfDef, Neigh.NoiseAround,Alarm.Vol) = =.90 if the Neighbor isn t def but he listen music (John case). =.70 if the Neighbor is def but his house is very quite (Mary case). Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 13 Relational Bayesian Networks Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 14

8 Dynamic Bayesian Networks: Extension of BN for modeling dynamic systems. State at time t represented by a set of random variables z t = (z 1,t,,z d,t ). The state at time t depends on the states at previous time steps. A 2TBN is a BN that contains variables from z t-1 whose parents are variables from z t and/or z t-1, and variables from z t without their parents. A 2TBN defines P(z t z t-1 ) by means of a directed acyclic graph (DAG) as follows: P(z t z t-1 ) = N i=1p(z i t Pa(z i t)) Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 15 Dynamic Bayesian Networks A Dynamic Bayesian Network (DBN) is defined to be a pair of Bayesian Networks (B 0, B ), where B 0 represents the initial distribution P(z 0 ), and B is a 2TBN, which defines the transition distribution P(z t+1 z t ). Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 16

9 Relational Dynamic Bayesian Nets: Once you defined a RBN and a DBN it is easy to define a RDBN... GUESS IT! Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 17 Particle ilters: Tecnique that implements a ricursive Bayesian Ilter through a Monte Carlo simulation. The key idea is to represent the posterior pdf with a set of random samples with associated weights and compute the estimation based on these samples and weights. As the number of samples becomes very large, this MC caratterization becomes an equivalent representation to the usual functional description of the posterior pdf, and the SIS algorithm filter approaches the optimal Bayesian estimate. Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 18

10 Particle iltering: steps ix the number of particles: M 1. Particle generation x ( m) k ~ p( xk xk 1) At time k arrives the observation/measure z k 2a. Weight computation w = p( z x *( m) ( m) k k k ) 2b. Weight normalization 3. Resampling w ( m) k = M w m= 1 *( m) k w *( m) k Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 19 Particle filtering operations Represents the required pdf by a set of samples with associated weights. Computs the estimate based in these samples and weights. Posterior pdf Sample space x ( m) k ~ p( xk xk 1 ) Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 20

11 Pros: Arbitrary pdf Most probable state-space Non-Gaussian noise More than one model Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 21 Cons: Computational complexity How to determine the number of particles Probable problems: density extraction, sampling variance Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 22

12 Rao-Blackwellized P: Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 23 Rao-Blackwellized P Rao-Blackwellization: Some components of the model can have a liner dynamic and can be estimate by a traditional Kalman ilter. Kalman ilter is combine with P to reduce the number of particles to be used for a satisfying performance. Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 24

13 Domingos P Complex & Simple Predicates Abstractions: (set of pairs of objects which are related in some way) P smoothing on an Abstraction lattice Cristina Manfredotti D.I.S.Co. Università di Milano - Bicocca 25

Bayesian Networks. Mausam (Slides by UW-AI faculty)

Bayesian Networks. Mausam (Slides by UW-AI faculty) Bayesian Networks Mausam (Slides by UW-AI faculty) Bayes Nets In general, joint distribution P over set of variables (X 1 x... x X n ) requires exponential space for representation & inference BNs provide

More information

An Introduction to the Use of Bayesian Network to Analyze Gene Expression Data

An Introduction to the Use of Bayesian Network to Analyze Gene Expression Data n Introduction to the Use of ayesian Network to nalyze Gene Expression Data Cristina Manfredotti Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co. Università degli Studi Milano-icocca

More information

Bayesian Networks Chapter 14. Mausam (Slides by UW-AI faculty & David Page)

Bayesian Networks Chapter 14. Mausam (Slides by UW-AI faculty & David Page) Bayesian Networks Chapter 14 Mausam (Slides by UW-AI faculty & David Page) Bayes Nets In general, joint distribution P over set of variables (X 1 x... x X n ) requires exponential space for representation

More information

13.3 Inference Using Full Joint Distribution

13.3 Inference Using Full Joint Distribution 191 The probability distribution on a single variable must sum to 1 It is also true that any joint probability distribution on any set of variables must sum to 1 Recall that any proposition a is equivalent

More information

Artificial Intelligence. Conditional probability. Inference by enumeration. Independence. Lesson 11 (From Russell & Norvig)

Artificial Intelligence. Conditional probability. Inference by enumeration. Independence. Lesson 11 (From Russell & Norvig) Artificial Intelligence Conditional probability Conditional or posterior probabilities e.g., cavity toothache) = 0.8 i.e., given that toothache is all I know tation for conditional distributions: Cavity

More information

Probability, Conditional Independence

Probability, Conditional Independence Probability, Conditional Independence June 19, 2012 Probability, Conditional Independence Probability Sample space Ω of events Each event ω Ω has an associated measure Probability of the event P(ω) Axioms

More information

Bayesian Networks. Read R&N Ch. 14.1-14.2. Next lecture: Read R&N 18.1-18.4

Bayesian Networks. Read R&N Ch. 14.1-14.2. Next lecture: Read R&N 18.1-18.4 Bayesian Networks Read R&N Ch. 14.1-14.2 Next lecture: Read R&N 18.1-18.4 You will be expected to know Basic concepts and vocabulary of Bayesian networks. Nodes represent random variables. Directed arcs

More information

CS 188: Artificial Intelligence. Probability recap

CS 188: Artificial Intelligence. Probability recap CS 188: Artificial Intelligence Bayes Nets Representation and Independence Pieter Abbeel UC Berkeley Many slides over this course adapted from Dan Klein, Stuart Russell, Andrew Moore Conditional probability

More information

Lecture 2: Introduction to belief (Bayesian) networks

Lecture 2: Introduction to belief (Bayesian) networks Lecture 2: Introduction to belief (Bayesian) networks Conditional independence What is a belief network? Independence maps (I-maps) January 7, 2008 1 COMP-526 Lecture 2 Recall from last time: Conditional

More information

Life of A Knowledge Base (KB)

Life of A Knowledge Base (KB) Life of A Knowledge Base (KB) A knowledge base system is a special kind of database management system to for knowledge base management. KB extraction: knowledge extraction using statistical models in NLP/ML

More information

Data Modeling & Analysis Techniques. Probability & Statistics. Manfred Huber 2011 1

Data Modeling & Analysis Techniques. Probability & Statistics. Manfred Huber 2011 1 Data Modeling & Analysis Techniques Probability & Statistics Manfred Huber 2011 1 Probability and Statistics Probability and statistics are often used interchangeably but are different, related fields

More information

Master s thesis tutorial: part III

Master s thesis tutorial: part III for the Autonomous Compliant Research group Tinne De Laet, Wilm Decré, Diederik Verscheure Katholieke Universiteit Leuven, Department of Mechanical Engineering, PMA Division 30 oktober 2006 Outline General

More information

Introduction to Mobile Robotics Bayes Filter Particle Filter and Monte Carlo Localization

Introduction to Mobile Robotics Bayes Filter Particle Filter and Monte Carlo Localization Introduction to Mobile Robotics Bayes Filter Particle Filter and Monte Carlo Localization Wolfram Burgard, Maren Bennewitz, Diego Tipaldi, Luciano Spinello 1 Motivation Recall: Discrete filter Discretize

More information

CURRICULUM VITAE. Ilaria.giordani@disco.unimib.it. Phd in computer science

CURRICULUM VITAE. Ilaria.giordani@disco.unimib.it. Phd in computer science CURRICULUM VITAE PERSONAL INFORMATION Name Address Giordani Ilaria Via Volturno 13 22063 Cantù (Co) Mobile phone number (+ 39) 333.8725026 Phone number (+ 39) 031.712957 E-mail Ilaria.giordani@disco.unimib.it

More information

Querying Joint Probability Distributions

Querying Joint Probability Distributions Querying Joint Probability Distributions Sargur Srihari srihari@cedar.buffalo.edu 1 Queries of Interest Probabilistic Graphical Models (BNs and MNs) represent joint probability distributions over multiple

More information

Chapter 28. Bayesian Networks

Chapter 28. Bayesian Networks Chapter 28. Bayesian Networks The Quest for Artificial Intelligence, Nilsson, N. J., 2009. Lecture Notes on Artificial Intelligence, Spring 2012 Summarized by Kim, Byoung-Hee and Lim, Byoung-Kwon Biointelligence

More information

EE 570: Location and Navigation

EE 570: Location and Navigation EE 570: Location and Navigation On-Line Bayesian Tracking Aly El-Osery 1 Stephen Bruder 2 1 Electrical Engineering Department, New Mexico Tech Socorro, New Mexico, USA 2 Electrical and Computer Engineering

More information

Monte Carlo-based statistical methods (MASM11/FMS091)

Monte Carlo-based statistical methods (MASM11/FMS091) Monte Carlo-based statistical methods (MASM11/FMS091) Jimmy Olsson Centre for Mathematical Sciences Lund University, Sweden Lecture 6 Sequential Monte Carlo methods II February 3, 2012 Changes in HA1 Problem

More information

Informatics 2D Reasoning and Agents Semester 2, 2015-16

Informatics 2D Reasoning and Agents Semester 2, 2015-16 Informatics 2D Reasoning and Agents Semester 2, 2015-16 Alex Lascarides alex@inf.ed.ac.uk Lecture 29 Decision Making Under Uncertainty 24th March 2016 Informatics UoE Informatics 2D 1 Where are we? Last

More information

An Introduction to Using WinBUGS for Cost-Effectiveness Analyses in Health Economics

An Introduction to Using WinBUGS for Cost-Effectiveness Analyses in Health Economics Slide 1 An Introduction to Using WinBUGS for Cost-Effectiveness Analyses in Health Economics Dr. Christian Asseburg Centre for Health Economics Part 1 Slide 2 Talk overview Foundations of Bayesian statistics

More information

Compression algorithm for Bayesian network modeling of binary systems

Compression algorithm for Bayesian network modeling of binary systems Compression algorithm for Bayesian network modeling of binary systems I. Tien & A. Der Kiureghian University of California, Berkeley ABSTRACT: A Bayesian network (BN) is a useful tool for analyzing the

More information

Big Data, Machine Learning, Causal Models

Big Data, Machine Learning, Causal Models Big Data, Machine Learning, Causal Models Sargur N. Srihari University at Buffalo, The State University of New York USA Int. Conf. on Signal and Image Processing, Bangalore January 2014 1 Plan of Discussion

More information

Model-based Synthesis. Tony O Hagan

Model-based Synthesis. Tony O Hagan Model-based Synthesis Tony O Hagan Stochastic models Synthesising evidence through a statistical model 2 Evidence Synthesis (Session 3), Helsinki, 28/10/11 Graphical modelling The kinds of models that

More information

A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking

A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking 174 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 2, FEBRUARY 2002 A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking M. Sanjeev Arulampalam, Simon Maskell, Neil

More information

STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

More information

Monte Carlo-based statistical methods (MASM11/FMS091)

Monte Carlo-based statistical methods (MASM11/FMS091) Monte Carlo-based statistical methods (MASM11/FMS091) Magnus Wiktorsson Centre for Mathematical Sciences Lund University, Sweden Lecture 6 Sequential Monte Carlo methods II February 7, 2014 M. Wiktorsson

More information

Bayesian Networks of Customer Satisfaction Survey Data

Bayesian Networks of Customer Satisfaction Survey Data Bayesian Networks of Customer Satisfaction Survey Data Silvia Salini * University of Milan, Italy Ron S. Kenett KPA Ltd., Raanana, Israel and University of Torino, Torino, Italy Abstract: A Bayesian Network

More information

Real-time Visual Tracker by Stream Processing

Real-time Visual Tracker by Stream Processing Real-time Visual Tracker by Stream Processing Simultaneous and Fast 3D Tracking of Multiple Faces in Video Sequences by Using a Particle Filter Oscar Mateo Lozano & Kuzahiro Otsuka presented by Piotr Rudol

More information

Tracking in flussi video 3D. Ing. Samuele Salti

Tracking in flussi video 3D. Ing. Samuele Salti Seminari XXIII ciclo Tracking in flussi video 3D Ing. Tutors: Prof. Tullio Salmon Cinotti Prof. Luigi Di Stefano The Tracking problem Detection Object model, Track initiation, Track termination, Tracking

More information

Performance evaluation of multi-camera visual tracking

Performance evaluation of multi-camera visual tracking Performance evaluation of multi-camera visual tracking Lucio Marcenaro, Pietro Morerio, Mauricio Soto, Andrea Zunino, Carlo S. Regazzoni DITEN, University of Genova Via Opera Pia 11A 16145 Genoa - Italy

More information

Spatial Statistics Chapter 3 Basics of areal data and areal data modeling

Spatial Statistics Chapter 3 Basics of areal data and areal data modeling Spatial Statistics Chapter 3 Basics of areal data and areal data modeling Recall areal data also known as lattice data are data Y (s), s D where D is a discrete index set. This usually corresponds to data

More information

The Basics of Graphical Models

The Basics of Graphical Models The Basics of Graphical Models David M. Blei Columbia University October 3, 2015 Introduction These notes follow Chapter 2 of An Introduction to Probabilistic Graphical Models by Michael Jordan. Many figures

More information

Vulnerabilità dei protocolli SSL/TLS

Vulnerabilità dei protocolli SSL/TLS Università degli Studi di Milano Facoltà di Scienze Matematiche, Fisiche e Naturali Dipartimento di Informatica e Comunicazione Vulnerabilità dei protocolli SSL/TLS Andrea Visconti Overview Introduction

More information

The Certainty-Factor Model

The Certainty-Factor Model The Certainty-Factor Model David Heckerman Departments of Computer Science and Pathology University of Southern California HMR 204, 2025 Zonal Ave Los Angeles, CA 90033 dheck@sumex-aim.stanford.edu 1 Introduction

More information

DDS-Enabled Cloud Management Support for Fast Task Offloading

DDS-Enabled Cloud Management Support for Fast Task Offloading DDS-Enabled Cloud Management Support for Fast Task Offloading IEEE ISCC 2012, Cappadocia Turkey Antonio Corradi 1 Luca Foschini 1 Javier Povedano-Molina 2 Juan M. Lopez-Soler 2 1 Dipartimento di Elettronica,

More information

References. Importance Sampling. Jessi Cisewski (CMU) Carnegie Mellon University. June 2014

References. Importance Sampling. Jessi Cisewski (CMU) Carnegie Mellon University. June 2014 Jessi Cisewski Carnegie Mellon University June 2014 Outline 1 Recall: Monte Carlo integration 2 3 Examples of (a) Monte Carlo, Monaco (b) Monte Carlo Casino Some content and examples from Wasserman (2004)

More information

Lezione 10 Introduzione a OPNET

Lezione 10 Introduzione a OPNET Corso di A.A. 2007-2008 Lezione 10 Introduzione a OPNET Ing. Marco GALEAZZI 1 What is OPNET? Con il nome OPNET viene indicata una suite di prodotti software sviluppati e commercializzati da OPNET Technologies,

More information

Chapter 14 Managing Operational Risks with Bayesian Networks

Chapter 14 Managing Operational Risks with Bayesian Networks Chapter 14 Managing Operational Risks with Bayesian Networks Carol Alexander This chapter introduces Bayesian belief and decision networks as quantitative management tools for operational risks. Bayesian

More information

Cell Phone based Activity Detection using Markov Logic Network

Cell Phone based Activity Detection using Markov Logic Network Cell Phone based Activity Detection using Markov Logic Network Somdeb Sarkhel sxs104721@utdallas.edu 1 Introduction Mobile devices are becoming increasingly sophisticated and the latest generation of smart

More information

Probabilistic Graphical Models

Probabilistic Graphical Models Probabilistic Graphical Models Raquel Urtasun and Tamir Hazan TTI Chicago April 4, 2011 Raquel Urtasun and Tamir Hazan (TTI-C) Graphical Models April 4, 2011 1 / 22 Bayesian Networks and independences

More information

Decision Trees and Networks

Decision Trees and Networks Lecture 21: Uncertainty 6 Today s Lecture Victor R. Lesser CMPSCI 683 Fall 2010 Decision Trees and Networks Decision Trees A decision tree is an explicit representation of all the possible scenarios from

More information

PTE505: Inverse Modeling for Subsurface Flow Data Integration (3 Units)

PTE505: Inverse Modeling for Subsurface Flow Data Integration (3 Units) PTE505: Inverse Modeling for Subsurface Flow Data Integration (3 Units) Instructor: Behnam Jafarpour, Mork Family Department of Chemical Engineering and Material Science Petroleum Engineering, HED 313,

More information

The Visualization Pipeline

The Visualization Pipeline The Visualization Pipeline Conceptual perspective Implementation considerations Algorithms used in the visualization Structure of the visualization applications Contents The focus is on presenting the

More information

Question 2 Naïve Bayes (16 points)

Question 2 Naïve Bayes (16 points) Question 2 Naïve Bayes (16 points) About 2/3 of your email is spam so you downloaded an open source spam filter based on word occurrences that uses the Naive Bayes classifier. Assume you collected the

More information

Feedforward Neural Networks and Backpropagation

Feedforward Neural Networks and Backpropagation Feedforward Neural Networks and Backpropagation Feedforward neural networks Architectural issues, computational capabilities Sigmoidal and radial basis functions Gradient-based learning and Backprogation

More information

Part III: Machine Learning. CS 188: Artificial Intelligence. Machine Learning This Set of Slides. Parameter Estimation. Estimation: Smoothing

Part III: Machine Learning. CS 188: Artificial Intelligence. Machine Learning This Set of Slides. Parameter Estimation. Estimation: Smoothing CS 188: Artificial Intelligence Lecture 20: Dynamic Bayes Nets, Naïve Bayes Pieter Abbeel UC Berkeley Slides adapted from Dan Klein. Part III: Machine Learning Up until now: how to reason in a model and

More information

Agenda. Interface Agents. Interface Agents

Agenda. Interface Agents. Interface Agents Agenda Marcelo G. Armentano Problem Overview Interface Agents Probabilistic approach Monitoring user actions Model of the application Model of user intentions Example Summary ISISTAN Research Institute

More information

MAP ESTIMATION WITH LASER SCANS BASED ON INCREMENTAL TREE NETWORK OPTIMIZER

MAP ESTIMATION WITH LASER SCANS BASED ON INCREMENTAL TREE NETWORK OPTIMIZER MAP ESTIMATION WITH LASER SCANS BASED ON INCREMENTAL TREE NETWORK OPTIMIZER Dario Lodi Rizzini 1, Stefano Caselli 1 1 Università degli Studi di Parma Dipartimento di Ingegneria dell Informazione viale

More information

QDquaderni. UP-DRES User Profiling for a Dynamic REcommendation System E. Messina, D. Toscani, F. Archetti. university of milano bicocca

QDquaderni. UP-DRES User Profiling for a Dynamic REcommendation System E. Messina, D. Toscani, F. Archetti. university of milano bicocca A01 084/01 university of milano bicocca QDquaderni department of informatics, systems and communication UP-DRES User Profiling for a Dynamic REcommendation System E. Messina, D. Toscani, F. Archetti research

More information

TUTORIAL MOVE 2009.1: 3D MODEL CONSTRUCTION FROM SURFACE GEOLOGICAL DATA

TUTORIAL MOVE 2009.1: 3D MODEL CONSTRUCTION FROM SURFACE GEOLOGICAL DATA UNIVERSITÁ DEGLI STUDI DI MILANO FACOLTÀ DI SCIENZE MATEMATICHE FISICHE E NATURALI DIPARTIMENTO DI SCIENZE DELLA TERRA ARDITO DESIO TUTORIAL MOVE 2009.1: 3D MODEL CONSTRUCTION FROM SURFACE GEOLOGICAL DATA

More information

ROBUST REAL-TIME ON-BOARD VEHICLE TRACKING SYSTEM USING PARTICLES FILTER. Ecole des Mines de Paris, Paris, France

ROBUST REAL-TIME ON-BOARD VEHICLE TRACKING SYSTEM USING PARTICLES FILTER. Ecole des Mines de Paris, Paris, France ROBUST REAL-TIME ON-BOARD VEHICLE TRACKING SYSTEM USING PARTICLES FILTER Bruno Steux Yotam Abramson Ecole des Mines de Paris, Paris, France Abstract: We describe a system for detection and tracking of

More information

UNIVERSITY OF LYON DOCTORAL SCHOOL OF COMPUTER SCIENCES AND MATHEMATICS P H D T H E S I S. Specialty : Computer Science. Author

UNIVERSITY OF LYON DOCTORAL SCHOOL OF COMPUTER SCIENCES AND MATHEMATICS P H D T H E S I S. Specialty : Computer Science. Author UNIVERSITY OF LYON DOCTORAL SCHOOL OF COMPUTER SCIENCES AND MATHEMATICS P H D T H E S I S Specialty : Computer Science Author Sérgio Rodrigues de Morais on November 16, 29 Bayesian Network Structure Learning

More information

Building Large-Scale Bayesian Networks

Building Large-Scale Bayesian Networks Building Large-Scale Bayesian Networks Martin Neil 1, Norman Fenton 1 and Lars Nielsen 2 1 Risk Assessment and Decision Analysis Research (RADAR) group, Computer Science Department, Queen Mary and Westfield

More information

Probabilistic Networks An Introduction to Bayesian Networks and Influence Diagrams

Probabilistic Networks An Introduction to Bayesian Networks and Influence Diagrams Probabilistic Networks An Introduction to Bayesian Networks and Influence Diagrams Uffe B. Kjærulff Department of Computer Science Aalborg University Anders L. Madsen HUGIN Expert A/S 10 May 2005 2 Contents

More information

5 Directed acyclic graphs

5 Directed acyclic graphs 5 Directed acyclic graphs (5.1) Introduction In many statistical studies we have prior knowledge about a temporal or causal ordering of the variables. In this chapter we will use directed graphs to incorporate

More information

Micro to Macro Equation-Free Bifurcation Analysis of Neuronal Random Graphs: Symmetry Breaking of Majority Rule Dynamics

Micro to Macro Equation-Free Bifurcation Analysis of Neuronal Random Graphs: Symmetry Breaking of Majority Rule Dynamics Micro to Macro Equation-Free Bifurcation Analysis of Neuronal Random Graphs: Symmetry Breang of Majority Rule Dynamics Konstantinos Spiliotis 1, Lucia Russo, Constantinos I. Siettos 1 1 School of Applied

More information

Bayesian networks - Time-series models - Apache Spark & Scala

Bayesian networks - Time-series models - Apache Spark & Scala Bayesian networks - Time-series models - Apache Spark & Scala Dr John Sandiford, CTO Bayes Server Data Science London Meetup - November 2014 1 Contents Introduction Bayesian networks Latent variables Anomaly

More information

Course: Model, Learning, and Inference: Lecture 5

Course: Model, Learning, and Inference: Lecture 5 Course: Model, Learning, and Inference: Lecture 5 Alan Yuille Department of Statistics, UCLA Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract Probability distributions on structured representation.

More information

DETERMINING THE CONDITIONAL PROBABILITIES IN BAYESIAN NETWORKS

DETERMINING THE CONDITIONAL PROBABILITIES IN BAYESIAN NETWORKS Hacettepe Journal of Mathematics and Statistics Volume 33 (2004), 69 76 DETERMINING THE CONDITIONAL PROBABILITIES IN BAYESIAN NETWORKS Hülya Olmuş and S. Oral Erbaş Received 22 : 07 : 2003 : Accepted 04

More information

A Statistical Framework for Operational Infrasound Monitoring

A Statistical Framework for Operational Infrasound Monitoring A Statistical Framework for Operational Infrasound Monitoring Stephen J. Arrowsmith Rod W. Whitaker LA-UR 11-03040 The views expressed here do not necessarily reflect the views of the United States Government,

More information

A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data.

A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data. 142 A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data. Denver Dash t Department of Physics and Astronomy and Decision Systems Laboratory University of Pittsburgh Pittsburgh,

More information

itesla Project Innovative Tools for Electrical System Security within Large Areas

itesla Project Innovative Tools for Electrical System Security within Large Areas itesla Project Innovative Tools for Electrical System Security within Large Areas Samir ISSAD RTE France samir.issad@rte-france.com PSCC 2014 Panel Session 22/08/2014 Advanced data-driven modeling techniques

More information

Network Tomography Based on to-end Measurements

Network Tomography Based on to-end Measurements Network Tomography Based on end-to to-end Measurements Francesco Lo Presti Dipartimento di Informatica - Università dell Aquila The First COST-IST(EU)-NSF(USA) Workshop on EXCHANGES & TRENDS IN NETWORKING

More information

Software and Hardware Solutions for Accurate Data and Profitable Operations. Miguel J. Donald J. Chmielewski Contributor. DuyQuang Nguyen Tanth

Software and Hardware Solutions for Accurate Data and Profitable Operations. Miguel J. Donald J. Chmielewski Contributor. DuyQuang Nguyen Tanth Smart Process Plants Software and Hardware Solutions for Accurate Data and Profitable Operations Miguel J. Bagajewicz, Ph.D. University of Oklahoma Donald J. Chmielewski Contributor DuyQuang Nguyen Tanth

More information

Stock Investing Using HUGIN Software

Stock Investing Using HUGIN Software Stock Investing Using HUGIN Software An Easy Way to Use Quantitative Investment Techniques Abstract Quantitative investment methods have gained foothold in the financial world in the last ten years. This

More information

Exam 3 Review/WIR 9 These problems will be started in class on April 7 and continued on April 8 at the WIR.

Exam 3 Review/WIR 9 These problems will be started in class on April 7 and continued on April 8 at the WIR. Exam 3 Review/WIR 9 These problems will be started in class on April 7 and continued on April 8 at the WIR. 1. Urn A contains 6 white marbles and 4 red marbles. Urn B contains 3 red marbles and two white

More information

Assistant Professor, Dipartimento di Matematica, Università di Genova.

Assistant Professor, Dipartimento di Matematica, Università di Genova. Alberto Sorrentino Dipartimento di Matematica, Università di Genova 16146 Genova H +39 349 8821450 T +39 010 353 6836 B sorrentino@dima.unige.it Position 1/2013 to date Assistant Professor, Dipartimento

More information

Gaussian Tail or Long Tail: On Error Characterization of MLC NAND Flash

Gaussian Tail or Long Tail: On Error Characterization of MLC NAND Flash Gaussian Tail or Long Tail: On Error Characterization of MLC NAND Flash Presented by: Shu-Yi Jack Wong Computer Engineering University of Toronto, Ontario, Canada Importance and Positioning A Multi-Level-Cell

More information

Advanced Linear Modeling

Advanced Linear Modeling Ronald Christensen Advanced Linear Modeling Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization Second Edition Springer Preface to the Second Edition

More information

Recursive Estimation

Recursive Estimation Recursive Estimation Raffaello D Andrea Spring 04 Problem Set : Bayes Theorem and Bayesian Tracking Last updated: March 8, 05 Notes: Notation: Unlessotherwisenoted,x, y,andz denoterandomvariables, f x

More information

International Journal of Software Engineering and Knowledge Engineering c World Scientific Publishing Company

International Journal of Software Engineering and Knowledge Engineering c World Scientific Publishing Company International Journal of Software Engineering and Knowledge Engineering c World Scientific Publishing Company Rapid Construction of Software Comprehension Tools WELF LÖWE Software Technology Group, MSI,

More information

Measuring the Power of a Test

Measuring the Power of a Test Textbook Reference: Chapter 9.5 Measuring the Power of a Test An economic problem motivates the statement of a null and alternative hypothesis. For a numeric data set, a decision rule can lead to the rejection

More information

SYSM 6304: Risk and Decision Analysis Lecture 5: Methods of Risk Analysis

SYSM 6304: Risk and Decision Analysis Lecture 5: Methods of Risk Analysis SYSM 6304: Risk and Decision Analysis Lecture 5: Methods of Risk Analysis M. Vidyasagar Cecil & Ida Green Chair The University of Texas at Dallas Email: M.Vidyasagar@utdallas.edu October 17, 2015 Outline

More information

Learning Instance-Specific Predictive Models

Learning Instance-Specific Predictive Models Journal of Machine Learning Research 11 (2010) 3333-3369 Submitted 3/09; Revised 7/10; Published 12/10 Learning Instance-Specific Predictive Models Shyam Visweswaran Gregory F. Cooper Department of Biomedical

More information

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes Knowledge Discovery and Data Mining Lecture 19 - Bagging Tom Kelsey School of Computer Science University of St Andrews http://tom.host.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-19-B &

More information

Global Optimisation of Neural Network Models Via Sequential Sampling

Global Optimisation of Neural Network Models Via Sequential Sampling Global Optimisation of Neural Network Models Via Sequential Sampling J oao FG de Freitas jfgf@eng.cam.ac.uk [Corresponding author] Mahesan Niranjan niranjan@eng.cam.ac.uk Arnaud Doucet ad2@eng.cam.ac.uk

More information

Monte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMS091)

Monte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMS091) Monte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMS091) Magnus Wiktorsson Centre for Mathematical Sciences Lund University, Sweden Lecture 5 Sequential Monte Carlo methods I February

More information

Big Data, Statistics, and the Internet

Big Data, Statistics, and the Internet Big Data, Statistics, and the Internet Steven L. Scott April, 4 Steve Scott (Google) Big Data, Statistics, and the Internet April, 4 / 39 Summary Big data live on more than one machine. Computing takes

More information

11. Time series and dynamic linear models

11. Time series and dynamic linear models 11. Time series and dynamic linear models Objective To introduce the Bayesian approach to the modeling and forecasting of time series. Recommended reading West, M. and Harrison, J. (1997). models, (2 nd

More information

Information Management course

Information Management course Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)

More information

Forecasting "High" and "Low" of financial time series by Particle systems and Kalman filters

Forecasting High and Low of financial time series by Particle systems and Kalman filters Forecasting "High" and "Low" of financial time series by Particle systems and Kalman filters S. DABLEMONT, S. VAN BELLEGEM, M. VERLEYSEN Université catholique de Louvain, Machine Learning Group, DICE 3,

More information

Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections

Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Maximilian Hung, Bohyun B. Kim, Xiling Zhang August 17, 2013 Abstract While current systems already provide

More information

Artificial Intelligence

Artificial Intelligence Artificial Intelligence ICS461 Fall 2010 1 Lecture #12B More Representations Outline Logics Rules Frames Nancy E. Reed nreed@hawaii.edu 2 Representation Agents deal with knowledge (data) Facts (believe

More information

10-601. Machine Learning. http://www.cs.cmu.edu/afs/cs/academic/class/10601-f10/index.html

10-601. Machine Learning. http://www.cs.cmu.edu/afs/cs/academic/class/10601-f10/index.html 10-601 Machine Learning http://www.cs.cmu.edu/afs/cs/academic/class/10601-f10/index.html Course data All up-to-date info is on the course web page: http://www.cs.cmu.edu/afs/cs/academic/class/10601-f10/index.html

More information

Project Management Multiple Resources Allocation

Project Management Multiple Resources Allocation EngOpt 2008 - International Conference on Engineering Optimization Rio de Janeiro, Brazil, 01-05 June 2008. Project Management Multiple Resources Allocation Anabela P. Tereso Madalena M. Araújo Rui S.

More information

Producing Accessible Slide Presentations for Scientific Lectures: a Case Study for the Italian University in the Mac OS X Environment

Producing Accessible Slide Presentations for Scientific Lectures: a Case Study for the Italian University in the Mac OS X Environment Producing Accessible Slide Presentations for Scientific Lectures: a Case Study for the Italian University in the Mac OS X Environment Valeria Brigatti - Ab.Acus, Milano, Italy Cristian Bernareggi - Biblioteca

More information

Introduction to Markov Chain Monte Carlo

Introduction to Markov Chain Monte Carlo Introduction to Markov Chain Monte Carlo Monte Carlo: sample from a distribution to estimate the distribution to compute max, mean Markov Chain Monte Carlo: sampling using local information Generic problem

More information

Generating Random Samples from the Generalized Pareto Mixture Model

Generating Random Samples from the Generalized Pareto Mixture Model Generating Random Samples from the Generalized Pareto Mixture Model MUSTAFA ÇAVUŞ AHMET SEZER BERNA YAZICI Department of Statistics Anadolu University Eskişehir 26470 TURKEY mustafacavus@anadolu.edu.tr

More information

Bayesian Tutorial (Sheet Updated 20 March)

Bayesian Tutorial (Sheet Updated 20 March) Bayesian Tutorial (Sheet Updated 20 March) Practice Questions (for discussing in Class) Week starting 21 March 2016 1. What is the probability that the total of two dice will be greater than 8, given that

More information

Detecting Spam in VoIP Networks. Ram Dantu Prakash Kolan

Detecting Spam in VoIP Networks. Ram Dantu Prakash Kolan Detecting Spam in VoIP Networks Ram Dantu Prakash Kolan More Multimedia Features Cost Why use VOIP? support for video-conferencing and video-phones Easier integration of voice with applications and databases

More information

Dependency Networks for Collaborative Filtering and Data Visualization

Dependency Networks for Collaborative Filtering and Data Visualization 264 UNCERTAINTY IN ARTIFICIAL INTELLIGENCE PROCEEDINGS 2000 Dependency Networks for Collaborative Filtering and Data Visualization David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite,

More information

Proactive Intention Recognition for Home Ambient Intelligence

Proactive Intention Recognition for Home Ambient Intelligence Proactive Intention Recognition for Home Ambient Intelligence Han The Anh and Luís Moniz Pereira CENTRIA UNL Artificial Intelligence Techniques for Ambience Intelligence Kuala Lumpur, July 18, 2010 Introduction

More information

I I I I I I I I I I I I I I I I I I I

I I I I I I I I I I I I I I I I I I I A ABSTRACT Method for Using Belief Networks as nfluence Diagrams Gregory F. Cooper Medical Computer Science Group Medical School Office Building Stanford University Stanford, CA 94305 This paper demonstrates

More information

2.3.4 Project planning

2.3.4 Project planning .. Project planning project consists of a set of m activities with their duration: activity i has duration d i, i =,..., m. estimate Some pairs of activities are subject to a precedence constraint: i j

More information

VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS

VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS Aswin C Sankaranayanan, Qinfen Zheng, Rama Chellappa University of Maryland College Park, MD - 277 {aswch, qinfen, rama}@cfar.umd.edu Volkan Cevher, James

More information

A robot s Navigation Problems. Localization. The Localization Problem. Sensor Readings

A robot s Navigation Problems. Localization. The Localization Problem. Sensor Readings A robot s Navigation Problems Localization Where am I? Localization Where have I been? Map making Where am I going? Mission planning What s the best way there? Path planning 1 2 The Localization Problem

More information

An introduction to Global Illumination. Tomas Akenine-Möller Department of Computer Engineering Chalmers University of Technology

An introduction to Global Illumination. Tomas Akenine-Möller Department of Computer Engineering Chalmers University of Technology An introduction to Global Illumination Tomas Akenine-Möller Department of Computer Engineering Chalmers University of Technology Isn t ray tracing enough? Effects to note in Global Illumination image:

More information

Parallelization Strategies for Multicore Data Analysis

Parallelization Strategies for Multicore Data Analysis Parallelization Strategies for Multicore Data Analysis Wei-Chen Chen 1 Russell Zaretzki 2 1 University of Tennessee, Dept of EEB 2 University of Tennessee, Dept. Statistics, Operations, and Management

More information

Monte Carlo-based statistical methods (MASM11/FMS091)

Monte Carlo-based statistical methods (MASM11/FMS091) Monte Carlo-based statistical methods (MASM11/FMS091) Jimmy Olsson Centre for Mathematical Sciences Lund University, Sweden Lecture 5 Sequential Monte Carlo methods I February 5, 2013 J. Olsson Monte Carlo-based

More information

Translating Stochastic CLS into Maude

Translating Stochastic CLS into Maude Translating Stochastic CLS into Maude (ONGOING WORK) Thomas Anung Basuki 1 Antonio Cerone 1 Paolo Milazzo 2 1. Int. Institute for Software Technology, United Nations Univeristy, Macau SAR, China 2. Dipartimento

More information