A Statistical Framework for Operational Infrasound Monitoring

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "A Statistical Framework for Operational Infrasound Monitoring"

Transcription

1 A Statistical Framework for Operational Infrasound Monitoring Stephen J. Arrowsmith Rod W. Whitaker LA-UR The views expressed here do not necessarily reflect the views of the United States Government, the United States Department of Energy, or the Los Alamos National Laboratory Slide 1

2 Overview Detection: The Adaptive F-detector Association Location: The Bayesian Infrasonic Source Locator (BISL) Case Study: Regional Infrasound Monitoring in Utah Conclusions InfraMonitor processing flowchart for 3 arrays Slide 2

3 The Adaptive F-detector The human eye is remarkably competent at detecting signals in noisy data (automatic algorithms must attempt to match this level of capability) Requirement: Hypothesis that can be tested Standard hypothesis: Noise is spatially incoherent This is frequently violated, leading to large numbers of spurious signals This hypothesis does not adapt to variations in ambient noise Advantages of an Adaptive F-detector: Does not require historical data Accounts for real ambient noise Can be applied operationally in real-time

4 The Adaptive F-detector Shumway et al. (1999): In the presence of stochastic correlated noise, F-statistic is distributed as: Null hypothesis violated Where: Conventional To estimate c (i.e., Ps/Pn), adaptively fit F distribution peak to Central F- distribution peak while processing data Null hypothesis revised based on actual noise Adaptive Apply p-value detection threshold (e.g., p = 0.01) Comparison between Adaptive F-detector and a conventional F-detector (gray boxes denote detections)

5 Association Problem: Identify groups of N arrivals that come from the same event Method: Grid search over region of interest, where, for each grid node: Search for groups of arrivals with backazimuths and delay-times (between arrays) consistent with that grid node Form associated detections for localization Gray areas represent grid nodes associated with test events (stars) at three arrays Slide 5

6 BISL Currently, the complexities involved in infrasound propagation favor a statistical approach over a deterministic approach. Deterministic / ray-tracing methods may not predict arrivals Lacking the ability to consistently predict arrivals, the Bayesian framework enables a probabilistic formulation on phase identification This information is incorporated through a Bayesian prior. Probability density functions for phase identification Slide 6

7 BISL: Notation The data used consist of: back azimuth vector arrival time vector θ =[θ 1,...,θ n ] t =[t 1,...,t n ] Model parameters consist of: x direction x 0 origin time t 0 y direction y 0 group velocity v Slide 7

8 Bayes theorem Using the notation introduced above, Bayes theorem takes the form: Posterior PDF Bayesian prior Likelihood equation Applying Bayes theorem requires distinguishing between a priori information and data. The former are incorporated into the Bayesian prior. The latter are incorporated into the likelihood equation. Slide 8

9 BISL: Synthetic Tests To test the algorithm, we performed a suite of tests using various synthetic configurations Here, we use the sample synthetic configuration shown below to illustrate the algorithm s capabilities Slide 9

10 BISL: Incorporating back azimuth data Back azimuth residual: Back azimuth likelihood component: Slide 10

11 Incorporating arrival time data Arrival time residual: Arrival time likelihood component: Slide 11

12

13 Utah event We further test the algorithm s performance using real infrasound data from an explosion at the UTTR test range Slide 13

14

15 BISL: Additional Algorithm Features In the case presented above, the use of Gaussian data error assumptions results in ellipsoidal credibility contours In general, the likelihood equations used need not be Gaussian and the credibility contours obtained need not be ellipsoidal Comparison of the two plots highlights the complementary nature of back azimuth and arrival time data for location constraint Slide 15

16 Case Study: Regional Infrasound Monitoring in Utah We have applied InfraMonitor to seven months of data from the Utah infrasound network 82 events at 4+ arrays 14 confirmed mining explosion detections 1 confirmed earthquake detection (red polygon) Clusters of events from military facilities Event locations in Utah (blue polygons) Slide 16

17 Case Study: The Jan 3rd, 2011 Circleville Earthquake The earthquake was detected at 6 arrays and missed by 3 This can be explained by propagation modeling UNCLASSIFIED Slide 17

18 Conclusions Infrasound monitoring algorithms have been developed from the ground up : Infrasound detection algorithms should be adaptive (accounting for changes in ambient noise) Infrasound location techniques need to account for uncertainties in atmospheric prediction models These techniques demonstrate that infrasound can be used for operational detection and location, at regional scales, with a low false alarm rate Testing of InfraMonitor in Utah missed no known ground-truth events, detecting signals from mining explosions, ordinance disposal shots, and a magnitude 4.7 earthquake Slide 18

Signal Detection. Outline. Detection Theory. Example Applications of Detection Theory

Signal Detection. Outline. Detection Theory. Example Applications of Detection Theory Outline Signal Detection M. Sami Fadali Professor of lectrical ngineering University of Nevada, Reno Hypothesis testing. Neyman-Pearson (NP) detector for a known signal in white Gaussian noise (WGN). Matched

More information

The CUSUM algorithm a small review. Pierre Granjon

The CUSUM algorithm a small review. Pierre Granjon The CUSUM algorithm a small review Pierre Granjon June, 1 Contents 1 The CUSUM algorithm 1.1 Algorithm............................... 1.1.1 The problem......................... 1.1. The different steps......................

More information

ELEC-E8104 Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems

ELEC-E8104 Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems Minimum Mean Square Error (MMSE) MMSE estimation of Gaussian random vectors Linear MMSE estimator for arbitrarily distributed

More information

Collision Probability Forecasting using a Monte Carlo Simulation. Matthew Duncan SpaceNav. Joshua Wysack SpaceNav

Collision Probability Forecasting using a Monte Carlo Simulation. Matthew Duncan SpaceNav. Joshua Wysack SpaceNav Collision Probability Forecasting using a Monte Carlo Simulation Matthew Duncan SpaceNav Joshua Wysack SpaceNav Joseph Frisbee United Space Alliance Space Situational Awareness is defined as the knowledge

More information

Least Squares Approach for Initial Data Recovery in Dynamic

Least Squares Approach for Initial Data Recovery in Dynamic Computing and Visualiation in Science manuscript No. (will be inserted by the editor) Least Squares Approach for Initial Data Recovery in Dynamic Data-Driven Applications Simulations C. Douglas,2, Y. Efendiev

More information

Summary of Probability

Summary of Probability Summary of Probability Mathematical Physics I Rules of Probability The probability of an event is called P(A), which is a positive number less than or equal to 1. The total probability for all possible

More information

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not. Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

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 Learning Based Method for Super-Resolution of Low Resolution Images

A Learning Based Method for Super-Resolution of Low Resolution Images A Learning Based Method for Super-Resolution of Low Resolution Images Emre Ugur June 1, 2004 emre.ugur@ceng.metu.edu.tr Abstract The main objective of this project is the study of a learning based method

More information

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

More information

67 Detection: Determining the Number of Sources

67 Detection: Determining the Number of Sources Williams, D.B. Detection: Determining the Number of Sources Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 999 c 999byCRCPressLLC 67 Detection:

More information

SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING

SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING AAS 07-228 SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING INTRODUCTION James G. Miller * Two historical uncorrelated track (UCT) processing approaches have been employed using general perturbations

More information

Fourth Cloud Retrieval Evaluation Workshop 4-7 March 2014, Grainau, Germany

Fourth Cloud Retrieval Evaluation Workshop 4-7 March 2014, Grainau, Germany Extending error characterization of cloud masking: Exploring the validity and usefulness of the SPARC-type and Naïve Bayesian probabilistic cloud masking methods Fourth Cloud Retrieval Evaluation Workshop

More information

Basics of Statistical Machine Learning

Basics of Statistical Machine Learning CS761 Spring 2013 Advanced Machine Learning Basics of Statistical Machine Learning Lecturer: Xiaojin Zhu jerryzhu@cs.wisc.edu Modern machine learning is rooted in statistics. You will find many familiar

More information

Spatial Accuracy Assessment of Digital Surface Models: A Probabilistic Approach

Spatial Accuracy Assessment of Digital Surface Models: A Probabilistic Approach Spatial Accuracy Assessment of Digital Surface Models: A Probabilistic Approach André Jalobeanu Centro de Geofisica de Evora, Portugal part of the SPACEFUSION Project (ANR, France) Outline Goals and objectives

More information

Lecture 8: Signal Detection and Noise Assumption

Lecture 8: Signal Detection and Noise Assumption ECE 83 Fall Statistical Signal Processing instructor: R. Nowak, scribe: Feng Ju Lecture 8: Signal Detection and Noise Assumption Signal Detection : X = W H : X = S + W where W N(, σ I n n and S = [s, s,...,

More information

Using Multiple Beams to Distinguish Radio Frequency Interference. from SETI Signals

Using Multiple Beams to Distinguish Radio Frequency Interference. from SETI Signals Using Multiple Beams to Distinguish Radio Frequency Interference from SETI Signals G. R. Harp Allen Telescope Array, SETI Institute, 235 Landings Dr., Mountain View, CA 9443 Abstract The Allen Telescope

More information

Exploiting A Constellation of Narrowband RF Sensors to Detect and Track Moving Targets

Exploiting A Constellation of Narrowband RF Sensors to Detect and Track Moving Targets Exploiting A Constellation of Narrowband RF Sensors to Detect and Track Moving Targets Chris Kreucher a, J. Webster Stayman b, Ben Shapo a, and Mark Stuff c a Integrity Applications Incorporated 900 Victors

More information

Tracking Algorithms. Lecture17: Stochastic Tracking. Joint Probability and Graphical Model. Probabilistic Tracking

Tracking Algorithms. Lecture17: Stochastic Tracking. Joint Probability and Graphical Model. Probabilistic Tracking Tracking Algorithms (2015S) Lecture17: Stochastic Tracking Bohyung Han CSE, POSTECH bhhan@postech.ac.kr Deterministic methods Given input video and current state, tracking result is always same. Local

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

Statistics Graduate Courses

Statistics Graduate Courses Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.

More information

Introduction to General and Generalized Linear Models

Introduction to General and Generalized Linear Models Introduction to General and Generalized Linear Models General Linear Models - part I Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby

More information

Advances in Network Performance Monitoring and Modelling

Advances in Network Performance Monitoring and Modelling Advances in Network Performance Monitoring and Modelling Presented by: Jerry Carter IDC/OD Contributors: Mark Prior IDC/SA Pierrick Mialle IDC/SA Mika Nikkinen IDC/SA Monika Krysta IDC/OD Preparatory Commission

More information

Data Science Center Eindhoven. Big Data: Challenges and Opportunities for Mathematicians. Alessandro Di Bucchianico

Data Science Center Eindhoven. Big Data: Challenges and Opportunities for Mathematicians. Alessandro Di Bucchianico Data Science Center Eindhoven Big Data: Challenges and Opportunities for Mathematicians Alessandro Di Bucchianico Dutch Mathematical Congress April 15, 2015 Contents 1. Big Data terminology 2. Various

More information

DATA ANALYTICS USING R

DATA ANALYTICS USING R DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data

More information

Radar Systems Engineering Lecture 6 Detection of Signals in Noise

Radar Systems Engineering Lecture 6 Detection of Signals in Noise Radar Systems Engineering Lecture 6 Detection of Signals in Noise Dr. Robert M. O Donnell Guest Lecturer Radar Systems Course 1 Detection 1/1/010 Block Diagram of Radar System Target Radar Cross Section

More information

False Discovery Rates

False Discovery Rates False Discovery Rates John D. Storey Princeton University, Princeton, USA January 2010 Multiple Hypothesis Testing In hypothesis testing, statistical significance is typically based on calculations involving

More information

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4. Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics

More information

Data Science at U of U

Data Science at U of U Data Science at U of U Je M. Phillips Assistant Professor, School of Computing Center for Extreme Data Management, Analysis, and Visualization Director, Data Management and Analysis Track University of

More information

Nonlinear Regression:

Nonlinear Regression: Zurich University of Applied Sciences School of Engineering IDP Institute of Data Analysis and Process Design Nonlinear Regression: A Powerful Tool With Considerable Complexity Half-Day : Improved Inference

More information

Time-frequency segmentation : statistical and local phase analysis

Time-frequency segmentation : statistical and local phase analysis Time-frequency segmentation : statistical and local phase analysis Florian DADOUCHI 1, Cornel IOANA 1, Julien HUILLERY 2, Cédric GERVAISE 1,3, Jérôme I. MARS 1 1 GIPSA-Lab, University of Grenoble 2 Ampère

More information

Multisensor Data Fusion and Applications

Multisensor Data Fusion and Applications Multisensor Data Fusion and Applications Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University 121 Link Hall Syracuse, New York 13244 USA E-mail: varshney@syr.edu

More information

Chapter ML:IV. IV. Statistical Learning. Probability Basics Bayes Classification Maximum a-posteriori Hypotheses

Chapter ML:IV. IV. Statistical Learning. Probability Basics Bayes Classification Maximum a-posteriori Hypotheses Chapter ML:IV IV. Statistical Learning Probability Basics Bayes Classification Maximum a-posteriori Hypotheses ML:IV-1 Statistical Learning STEIN 2005-2015 Area Overview Mathematics Statistics...... Stochastics

More information

Multivariate Analysis of Ecological Data

Multivariate Analysis of Ecological Data Multivariate Analysis of Ecological Data MICHAEL GREENACRE Professor of Statistics at the Pompeu Fabra University in Barcelona, Spain RAUL PRIMICERIO Associate Professor of Ecology, Evolutionary Biology

More information

WE consider a decentralized detection problem and study

WE consider a decentralized detection problem and study IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 59, NO. 4, APRIL 2011 1759 On Decentralized Detection With Partial Information Sharing Among Sensors O. Patrick Kreidl, John N. Tsitsiklis, Fellow, IEEE, and

More information

Supplement to Call Centers with Delay Information: Models and Insights

Supplement to Call Centers with Delay Information: Models and Insights Supplement to Call Centers with Delay Information: Models and Insights Oualid Jouini 1 Zeynep Akşin 2 Yves Dallery 1 1 Laboratoire Genie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290

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

Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021 Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class

More information

Sampling and Hypothesis Testing

Sampling and Hypothesis Testing Population and sample Sampling and Hypothesis Testing Allin Cottrell Population : an entire set of objects or units of observation of one sort or another. Sample : subset of a population. Parameter versus

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

The Image Deblurring Problem

The Image Deblurring Problem page 1 Chapter 1 The Image Deblurring Problem You cannot depend on your eyes when your imagination is out of focus. Mark Twain When we use a camera, we want the recorded image to be a faithful representation

More information

Bayesian Statistics: Indian Buffet Process

Bayesian Statistics: Indian Buffet Process Bayesian Statistics: Indian Buffet Process Ilker Yildirim Department of Brain and Cognitive Sciences University of Rochester Rochester, NY 14627 August 2012 Reference: Most of the material in this note

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

Hypothesis Testing. 1 Introduction. 2 Hypotheses. 2.1 Null and Alternative Hypotheses. 2.2 Simple vs. Composite. 2.3 One-Sided and Two-Sided Tests

Hypothesis Testing. 1 Introduction. 2 Hypotheses. 2.1 Null and Alternative Hypotheses. 2.2 Simple vs. Composite. 2.3 One-Sided and Two-Sided Tests Hypothesis Testing 1 Introduction This document is a simple tutorial on hypothesis testing. It presents the basic concepts and definitions as well as some frequently asked questions associated with hypothesis

More information

Chapter 4: Constrained estimators and tests in the multiple linear regression model (Part II)

Chapter 4: Constrained estimators and tests in the multiple linear regression model (Part II) Chapter 4: Constrained estimators and tests in the multiple linear regression model (Part II) Florian Pelgrin HEC September-December 2010 Florian Pelgrin (HEC) Constrained estimators September-December

More information

Jiří Matas. Hough Transform

Jiří Matas. Hough Transform Hough Transform Jiří Matas Center for Machine Perception Department of Cybernetics, Faculty of Electrical Engineering Czech Technical University, Prague Many slides thanks to Kristen Grauman and Bastian

More information

Fairfield Public Schools

Fairfield Public Schools Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity

More information

Confidence intervals, t tests, P values

Confidence intervals, t tests, P values Confidence intervals, t tests, P values Joe Felsenstein Department of Genome Sciences and Department of Biology Confidence intervals, t tests, P values p.1/31 Normality Everybody believes in the normal

More information

1 General information

1 General information SGN-1650/SGN-1656 Signal Processing Laboratory Constructing 3D model from stereo image pair 1 General information In this exercise, a 3D model is constructed using stereo image pair. The exercise work

More information

MODELLING OCCUPATIONAL EXPOSURE USING A RANDOM EFFECTS MODEL: A BAYESIAN APPROACH ABSTRACT

MODELLING OCCUPATIONAL EXPOSURE USING A RANDOM EFFECTS MODEL: A BAYESIAN APPROACH ABSTRACT MODELLING OCCUPATIONAL EXPOSURE USING A RANDOM EFFECTS MODEL: A BAYESIAN APPROACH Justin Harvey * and Abrie van der Merwe ** * Centre for Statistical Consultation, University of Stellenbosch ** University

More information

Joint parameter and state estimation algorithms for real-time traffic monitoring

Joint parameter and state estimation algorithms for real-time traffic monitoring USDOT Region V Regional University Transportation Center Final Report NEXTRANS Project No 097IY04. Joint parameter and state estimation algorithms for real-time traffic monitoring By Ren Wang PhD Candidate

More information

Tutorial 5: Hypothesis Testing

Tutorial 5: Hypothesis Testing Tutorial 5: Hypothesis Testing Rob Nicholls nicholls@mrc-lmb.cam.ac.uk MRC LMB Statistics Course 2014 Contents 1 Introduction................................ 1 2 Testing distributional assumptions....................

More information

Location of Mobile Terminals Using Time Measurements and Survey Points

Location of Mobile Terminals Using Time Measurements and Survey Points IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 52, NO. 4, JULY 2003 999 Location of Mobile Terminals Using Time Measurements and Survey Points Michael McGuire, Member, IEEE, Konstantinos N. Plataniotis,

More information

Fuzzy Probability Distributions in Bayesian Analysis

Fuzzy Probability Distributions in Bayesian Analysis Fuzzy Probability Distributions in Bayesian Analysis Reinhard Viertl and Owat Sunanta Department of Statistics and Probability Theory Vienna University of Technology, Vienna, Austria Corresponding author:

More information

CS 5410 - Computer and Network Security: Intrusion Detection

CS 5410 - Computer and Network Security: Intrusion Detection CS 5410 - Computer and Network Security: Intrusion Detection Professor Kevin Butler Fall 2015 Locked Down You re using all the techniques we will talk about over the course of the semester: Strong access

More information

Yiming Peng, Department of Statistics. February 12, 2013

Yiming Peng, Department of Statistics. February 12, 2013 Regression Analysis Using JMP Yiming Peng, Department of Statistics February 12, 2013 2 Presentation and Data http://www.lisa.stat.vt.edu Short Courses Regression Analysis Using JMP Download Data to Desktop

More information

Inference from sub-nyquist Samples

Inference from sub-nyquist Samples Inference from sub-nyquist Samples Alireza Razavi, Mikko Valkama Department of Electronics and Communications Engineering/TUT Characteristics of Big Data (4 V s) Volume: Traditional computing methods are

More information

Message-passing sequential detection of multiple change points in networks

Message-passing sequential detection of multiple change points in networks Message-passing sequential detection of multiple change points in networks Long Nguyen, Arash Amini Ram Rajagopal University of Michigan Stanford University ISIT, Boston, July 2012 Nguyen/Amini/Rajagopal

More information

Probabilistic Latent Semantic Analysis (plsa)

Probabilistic Latent Semantic Analysis (plsa) Probabilistic Latent Semantic Analysis (plsa) SS 2008 Bayesian Networks Multimedia Computing, Universität Augsburg Rainer.Lienhart@informatik.uni-augsburg.de www.multimedia-computing.{de,org} References

More information

Lecture 11: Graphical Models for Inference

Lecture 11: Graphical Models for Inference Lecture 11: Graphical Models for Inference So far we have seen two graphical models that are used for inference - the Bayesian network and the Join tree. These two both represent the same joint probability

More information

Likelihood: Frequentist vs Bayesian Reasoning

Likelihood: Frequentist vs Bayesian Reasoning "PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B University of California, Berkeley Spring 2009 N Hallinan Likelihood: Frequentist vs Bayesian Reasoning Stochastic odels and

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 Detection Theory

Introduction to Detection Theory Introduction to Detection Theory Reading: Ch. 3 in Kay-II. Notes by Prof. Don Johnson on detection theory, see http://www.ece.rice.edu/~dhj/courses/elec531/notes5.pdf. Ch. 10 in Wasserman. EE 527, Detection

More information

The ERP Boot Camp! ERP Localization!

The ERP Boot Camp! ERP Localization! The! ERP Localization! All slides S. J. Luck, except as indicated in the notes sections of individual slides! Slides may be used for nonprofit educational purposes if this copyright notice is included,

More information

The Wondrous World of fmri statistics

The Wondrous World of fmri statistics Outline The Wondrous World of fmri statistics FMRI data and Statistics course, Leiden, 11-3-2008 The General Linear Model Overview of fmri data analysis steps fmri timeseries Modeling effects of interest

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

Neural Decoding of Cursor Motion Using a Kalman Filter

Neural Decoding of Cursor Motion Using a Kalman Filter Neural Decoding of Cursor Motion Using a Kalman Filter W. Wu M. J. Black Y. Gao E. Bienenstock M. Serruya A. Shaikhouni J. P. Donoghue Division of Applied Mathematics, Dept. of Computer Science, Dept.

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

Density Level Detection is Classification

Density Level Detection is Classification Density Level Detection is Classification Ingo Steinwart, Don Hush and Clint Scovel Modeling, Algorithms and Informatics Group, CCS-3 Los Alamos National Laboratory {ingo,dhush,jcs}@lanl.gov Abstract We

More information

A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions

A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions Jing Gao Wei Fan Jiawei Han Philip S. Yu University of Illinois at Urbana-Champaign IBM T. J. Watson Research Center

More information

What is Bayesian statistics and why everything else is wrong

What is Bayesian statistics and why everything else is wrong What is Bayesian statistics and why everything else is wrong 1 Michael Lavine ISDS, Duke University, Durham, North Carolina Abstract We use a single example to explain (1), the Likelihood Principle, (2)

More information

Statistics in Astronomy

Statistics in Astronomy Statistics in Astronomy Initial question: How do you maximize the information you get from your data? Statistics is an art as well as a science, so it s important to use it as you would any other tool:

More information

Crater detection with segmentation-based image processing algorithm

Crater detection with segmentation-based image processing algorithm Template reference : 100181708K-EN Crater detection with segmentation-based image processing algorithm M. Spigai, S. Clerc (Thales Alenia Space-France) V. Simard-Bilodeau (U. Sherbrooke and NGC Aerospace,

More information

Multivariate Normal Distribution

Multivariate Normal Distribution Multivariate Normal Distribution Lecture 4 July 21, 2011 Advanced Multivariate Statistical Methods ICPSR Summer Session #2 Lecture #4-7/21/2011 Slide 1 of 41 Last Time Matrices and vectors Eigenvalues

More information

Logic, Probability and Learning

Logic, Probability and Learning Logic, Probability and Learning Luc De Raedt luc.deraedt@cs.kuleuven.be Overview Logic Learning Probabilistic Learning Probabilistic Logic Learning Closely following : Russell and Norvig, AI: a modern

More information

Applications to Data Smoothing and Image Processing I

Applications to Data Smoothing and Image Processing I Applications to Data Smoothing and Image Processing I MA 348 Kurt Bryan Signals and Images Let t denote time and consider a signal a(t) on some time interval, say t. We ll assume that the signal a(t) is

More information

Christfried Webers. Canberra February June 2015

Christfried Webers. Canberra February June 2015 c Statistical Group and College of Engineering and Computer Science Canberra February June (Many figures from C. M. Bishop, "Pattern Recognition and ") 1of 829 c Part VIII Linear Classification 2 Logistic

More information

Cyber-Security Analysis of State Estimators in Power Systems

Cyber-Security Analysis of State Estimators in Power Systems Cyber-Security Analysis of State Estimators in Electric Power Systems André Teixeira 1, Saurabh Amin 2, Henrik Sandberg 1, Karl H. Johansson 1, and Shankar Sastry 2 ACCESS Linnaeus Centre, KTH-Royal Institute

More information

De-ghosting by kurtosis maximisation in practice Sergio Grion*, Rob Telling and Janet Barnes, Dolphin Geophysical

De-ghosting by kurtosis maximisation in practice Sergio Grion*, Rob Telling and Janet Barnes, Dolphin Geophysical in practice Sergio Grion*, Rob Telling and Janet Barnes, Dolphin Geophysical Summary Adaptive de-ghosting estimates the parameters of the physical process determining the ghost reflection, which are not

More information

Time Series Analysis: Basic Forecasting.

Time Series Analysis: Basic Forecasting. Time Series Analysis: Basic Forecasting. As published in Benchmarks RSS Matters, April 2015 http://web3.unt.edu/benchmarks/issues/2015/04/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD jonathan.starkweather@unt.edu

More information

MUSIC-like Processing of Pulsed Continuous Wave Signals in Active Sonar Experiments

MUSIC-like Processing of Pulsed Continuous Wave Signals in Active Sonar Experiments 23rd European Signal Processing Conference EUSIPCO) MUSIC-like Processing of Pulsed Continuous Wave Signals in Active Sonar Experiments Hock Siong LIM hales Research and echnology, Singapore hales Solutions

More information

What is Modeling and Simulation and Software Engineering?

What is Modeling and Simulation and Software Engineering? What is Modeling and Simulation and Software Engineering? V. Sundararajan Scientific and Engineering Computing Group Centre for Development of Advanced Computing Pune 411 007 vsundar@cdac.in Definitions

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

CCNY. BME I5100: Biomedical Signal Processing. Linear Discrimination. Lucas C. Parra Biomedical Engineering Department City College of New York

CCNY. BME I5100: Biomedical Signal Processing. Linear Discrimination. Lucas C. Parra Biomedical Engineering Department City College of New York BME I5100: Biomedical Signal Processing Linear Discrimination Lucas C. Parra Biomedical Engineering Department CCNY 1 Schedule Week 1: Introduction Linear, stationary, normal - the stuff biology is not

More information

Linear Models for Classification

Linear Models for Classification Linear Models for Classification Sumeet Agarwal, EEL709 (Most figures from Bishop, PRML) Approaches to classification Discriminant function: Directly assigns each data point x to a particular class Ci

More information

SOLiD System accuracy with the Exact Call Chemistry module

SOLiD System accuracy with the Exact Call Chemistry module WHITE PPER 55 Series SOLiD System SOLiD System accuracy with the Exact all hemistry module ONTENTS Principles of Exact all hemistry Introduction Encoding of base sequences with Exact all hemistry Demonstration

More information

A Movement Tracking Management Model with Kalman Filtering Global Optimization Techniques and Mahalanobis Distance

A Movement Tracking Management Model with Kalman Filtering Global Optimization Techniques and Mahalanobis Distance Loutraki, 21 26 October 2005 A Movement Tracking Management Model with ing Global Optimization Techniques and Raquel Ramos Pinho, João Manuel R. S. Tavares, Miguel Velhote Correia Laboratório de Óptica

More information

Monte Carlo testing with Big Data

Monte Carlo testing with Big Data Monte Carlo testing with Big Data Patrick Rubin-Delanchy University of Bristol & Heilbronn Institute for Mathematical Research Joint work with: Axel Gandy (Imperial College London) with contributions from:

More information

Sampling via Moment Sharing: A New Framework for Distributed Bayesian Inference for Big Data

Sampling via Moment Sharing: A New Framework for Distributed Bayesian Inference for Big Data Sampling via Moment Sharing: A New Framework for Distributed Bayesian Inference for Big Data (Oxford) in collaboration with: Minjie Xu, Jun Zhu, Bo Zhang (Tsinghua) Balaji Lakshminarayanan (Gatsby) Bayesian

More information

Anomaly detection for Big Data, networks and cyber-security

Anomaly detection for Big Data, networks and cyber-security Anomaly detection for Big Data, networks and cyber-security Patrick Rubin-Delanchy University of Bristol & Heilbronn Institute for Mathematical Research Joint work with Nick Heard (Imperial College London),

More information

Tutorial on Markov Chain Monte Carlo

Tutorial on Markov Chain Monte Carlo Tutorial on Markov Chain Monte Carlo Kenneth M. Hanson Los Alamos National Laboratory Presented at the 29 th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Technology,

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

An Adaptive Index Structure for High-Dimensional Similarity Search

An Adaptive Index Structure for High-Dimensional Similarity Search An Adaptive Index Structure for High-Dimensional Similarity Search Abstract A practical method for creating a high dimensional index structure that adapts to the data distribution and scales well with

More information

PHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY

PHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY PHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY V. Knyaz a, *, Yu. Visilter, S. Zheltov a State Research Institute for Aviation System (GosNIIAS), 7, Victorenko str., Moscow, Russia

More information

On Decentralized Detection with Partial Information Sharing among Sensors

On Decentralized Detection with Partial Information Sharing among Sensors On Decentralized Detection with Partial Information Sharing among Sensors The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation

More information

Notes for STA 437/1005 Methods for Multivariate Data

Notes for STA 437/1005 Methods for Multivariate Data Notes for STA 437/1005 Methods for Multivariate Data Radford M. Neal, 26 November 2010 Random Vectors Notation: Let X be a random vector with p elements, so that X = [X 1,..., X p ], where denotes transpose.

More information

15.0 More Hypothesis Testing

15.0 More Hypothesis Testing 15.0 More Hypothesis Testing 1 Answer Questions Type I and Type II Error Power Calculation Bayesian Hypothesis Testing 15.1 Type I and Type II Error In the philosophy of hypothesis testing, the null hypothesis

More information

A crash course in probability and Naïve Bayes classification

A crash course in probability and Naïve Bayes classification Probability theory A crash course in probability and Naïve Bayes classification Chapter 9 Random variable: a variable whose possible values are numerical outcomes of a random phenomenon. s: A person s

More information

High Quality Image Deblurring Panchromatic Pixels

High Quality Image Deblurring Panchromatic Pixels High Quality Image Deblurring Panchromatic Pixels ACM Transaction on Graphics vol. 31, No. 5, 2012 Sen Wang, Tingbo Hou, John Border, Hong Qin, and Rodney Miller Presented by Bong-Seok Choi School of Electrical

More information

Data Mining: Exploring Data. Lecture Notes for Chapter 3. Slides by Tan, Steinbach, Kumar adapted by Michael Hahsler

Data Mining: Exploring Data. Lecture Notes for Chapter 3. Slides by Tan, Steinbach, Kumar adapted by Michael Hahsler Data Mining: Exploring Data Lecture Notes for Chapter 3 Slides by Tan, Steinbach, Kumar adapted by Michael Hahsler Topics Exploratory Data Analysis Summary Statistics Visualization What is data exploration?

More information