The SIS Epidemic Model with Markovian Switching
|
|
- Edward Palmer
- 7 years ago
- Views:
Transcription
1 The SIS Epidemic with Markovian Switching Department of Mathematics and Statistics University of Strathclyde Glasgow, G1 1XH (Joint work with A. Gray, D. Greenhalgh and J. Pan)
2 Outline Motivation 1 Motivation Prey-predator model Prey-predator model with Markovian switching SIS 2 3
3 Outline Motivation Prey-predator model Prey-predator model with Markovian switching SIS 1 Motivation Prey-predator model Prey-predator model with Markovian switching SIS 2 3
4 Prey-predator model Prey-predator model with Markovian switching SIS The prey-predator model is described by ẋ 1 (t) = x 1 (t)(a 1 b 1 x 2 (t)), ẋ 2 (t) = x 2 (t)( c 1 + d 1 x 1 (t)), (1.1) on t 0, where x 1 (t) and x 2 (t) are the numbers of preys and predators.
5 Prey-predator model Prey-predator model with Markovian switching SIS Noting we have dx 1 (t) dx 2 (t) = x 1(t)(a 1 b 1 x 2 (t)) x 2 (t)( c 1 + d 1 x 1 (t)), c 1 + d 1 x 1 (t) x 1 (t) dx 1 (t) = a 1 b 1 x 2 (t) dx 2 (t). x 2 (t) Integrating yields c 1 log(x 1 (t)) + d 1 x 1 (t) a 1 log(x 2 (t)) + b 1 x 2 (t) = const. This implies the solution is periodic.
6 Prey-predator model Prey-predator model with Markovian switching SIS Example in mode 1: ẋ 1 (t) = x 1 (t)(1 2x 2 (t)), ẋ 2 (t) = x 2 (t)( 1 + x 1 (t)) (1.2) with x 1 (0) = 1 and x 2 (0) = 2. The solution is shown in Figure 1.
7 Prey-predator model Prey-predator model with Markovian switching SIS States x1 x2 x Time x1 Figure 1
8 Prey-predator model Prey-predator model with Markovian switching SIS Example in mode 2: ẋ 1 (t) = x 1 (t)(0.5 5x 2 (t)), ẋ 2 (t) = x 2 (t)( 6 + 2x 1 (t)) (1.3) with x 1 (0) = 1 and x 2 (0) = 2. The solution is shown in Figure 2.
9 Prey-predator model Prey-predator model with Markovian switching SIS States x1 x2 x Time x1 Figure 2
10 Outline Motivation Prey-predator model Prey-predator model with Markovian switching SIS 1 Motivation Prey-predator model Prey-predator model with Markovian switching SIS 2 3
11 Prey-predator model Prey-predator model with Markovian switching SIS The prey-predator model is now switching from mode 1 or 2 to the other according to the continuous-time Markov chain r(t) on the state space S = {1, 2} with the with the generator ( ) 3 3 Γ = 1 1 starting from r(0) = 1. So the model becomes where ẋ 1 (t) = x 1 (t)(a r(t) b r(t) x 2 (t)), ẋ 2 (t) = x 2 (t)( c r(t) + d r(t) x 1 (t)), (1.4) a 1 = 1, b 1 = 2, c 1 = 1, d 1 = 1; a 2 = 0.5, b 2 = 5, c 2 = 6, d 2 = 2.
12 Prey-predator model Prey-predator model with Markovian switching SIS Takeuchi et al. (2006) revealed a very interesting and surprising result: all positive trajectories of equation (1.4) always exit from any compact set of R 2 + with probability 1; that is equation (1.4) is neither permanent nor dissipative. Figure 3 supports this result clearly.
13 Prey-predator model Prey-predator model with Markovian switching SIS r(t) t States x1 x t Figure 3
14 Outline Motivation Prey-predator model Prey-predator model with Markovian switching SIS 1 Motivation Prey-predator model Prey-predator model with Markovian switching SIS 2 3
15 Prey-predator model Prey-predator model with Markovian switching SIS The classical SIS epidemic model is described by { ds(t) dt di(t) dt = µn βs(t)i(t) + γi(t) µs(t), = βs(t)i(t) (µ + γ)i(t), subject to S(t) + I(t) = N, along with the initial values S(0) = S 0 > 0 and I(0) = I 0 > 0, where I(t) and S(t) are respectively the number of infectious and susceptible individuals at time t in a population of size N, and µ and γ 1 are the average death rate and the average infectious period respectively. β is the disease transmission coefficient, so that β = λ/n, where λ is the disease contact rate of an infective individual. (1.5)
16 Prey-predator model Prey-predator model with Markovian switching SIS It is easy to see that I(t) obeys the scalar Lotka Volterra model di(t) dt = I(t)[βN µ γ βi(t)], (1.6) which has the explicit solution [ ( ) ] e (βn µ γ)t 1 β β 1, I I(t) = 0 βn µ γ + βn µ γ [ ] 1 1, (1.7) + βt I0 if βn µ γ 0 and = 0, respectively. Defining the basic reproduction number for the deterministic SIS model we can conclude: R D 0 = βn µ + γ, (1.8)
17 Prey-predator model Prey-predator model with Markovian switching SIS If R D 0 1, lim t I(t) = 0. If R0 D > 1, lim t I(t) = βn µ γ β. In this case, I(t) will monotonically decrease or increase to βn µ γ β if I(0) > βn µ γ β I(t) βn µ γ β or < βn µ γ β if I(0) = βn µ γ β., respectively, while
18 Prey-predator model Prey-predator model with Markovian switching SIS If R D 0 1, lim t I(t) = 0. If R0 D > 1, lim t I(t) = βn µ γ β. In this case, I(t) will monotonically decrease or increase to βn µ γ β if I(0) > βn µ γ β I(t) βn µ γ β or < βn µ γ β if I(0) = βn µ γ β., respectively, while
19 Outline Motivation 1 Motivation Prey-predator model Prey-predator model with Markovian switching SIS 2 3
20 The stochastic has the form { ds(t) dt di(t) dt = µ r(t) N β r(t) S(t)I(t) + γ r(t) I(t) µ r(t) S(t), = β r(t) S(t)I(t) (µ r(t) + γ r(t) )I(t), (2.1) where r(t) is a Markov chain on the state space S = {1, 2} with the generator ( ) ν12 ν Γ = 12. ν 21 ν 21
21 There is a sequence {τ k } k 0 of finite-valued F t -stopping times such that 0 = τ 0 < τ 1 < < τ k almost surely and r(t) = r(τ k )I [τk,τ k+1 )(t). (2.2) k=0 Moreover, given that r(τ k ) = 1, the random variable τ k+1 τ k follows the exponential distribution with parameter ν 12, while given that r(τ k ) = 2, τ k+1 τ k follows the exponential distribution with parameter ν 21. Furthermore, this Markov chain has a unique stationary distribution Π = (π 1, π 2 ) given by π 1 = ν 21 ν 12 + ν 21, π 2 = ν 12 ν 12 + ν 21. (2.3)
22 We assume that the system parameters β i, µ i, γ i (i S) are all positive numbers. Given that I(t) + S(t) = N, we see that I(t), the number of infectious individuals, obeys the stochastic Lotka Volterra model with Markovian switching given by where di(t) dt = I(t)[α r(t) β r(t) I(t)], (2.4) α i := β i N µ i γ i, i S. (2.5)
23 Theorem For any given initial value I(0) = I 0 (0, N), there is a unique solution I(t) on t R + to equation (2.4) such that P(I(t) (0, N) for all t 0) = 1. Moreover, the solution has the explicit form ( ) t exp 0 α r(s)ds I(t) = 1 I 0 + ( t ). (2.6) 0 exp s 0 α r(u)du β r(s) ds
24 Outline Motivation 1 Motivation Prey-predator model Prey-predator model with Markovian switching SIS 2 3
25 Recall that for the deterministic SIS epidemic model (1.6), the basic reproduction number R0 D was also the threshold between disease extinction and persistence, with extinction for R0 D 1 and persistence for R0 D > 1. In the stochastic model, there are different types of extinction and persistence, for example almost sure extinction, extinction in mean square and extinction in probability. In the rest of the paper we examine a threshold T S 0 = π 1 β 1 N + π 2 β 2 N π 1 (µ 1 + γ 1 ) + π 2 (µ 2 + γ 2 ) (2.7) for almost sure extinction or persistence of our stochastic epidemic model.
26 Proposition We have the following alternative condition on the value of T0 S: T0 S < 1 if and only if π 1α 1 + π 2 α 2 < 0; T0 S = 1 if and only if π 1α 1 + π 2 α 2 = 0; T0 S > 1 if and only if π 1α 1 + π 2 α 2 > 0.
27 Proposition We have the following alternative condition on the value of T0 S: T0 S < 1 if and only if π 1α 1 + π 2 α 2 < 0; T0 S = 1 if and only if π 1α 1 + π 2 α 2 = 0; T0 S > 1 if and only if π 1α 1 + π 2 α 2 > 0.
28 Proposition We have the following alternative condition on the value of T0 S: T0 S < 1 if and only if π 1α 1 + π 2 α 2 < 0; T0 S = 1 if and only if π 1α 1 + π 2 α 2 = 0; T0 S > 1 if and only if π 1α 1 + π 2 α 2 > 0.
29 Proposition We have the following alternative condition on the value of T0 S: T0 S < 1 if and only if π 1α 1 + π 2 α 2 < 0; T0 S = 1 if and only if π 1α 1 + π 2 α 2 = 0; T0 S > 1 if and only if π 1α 1 + π 2 α 2 > 0.
30 Theorem If T0 S < 1, then, for any given initial value I 0 (0, N), the solution of the stochastic SIS epidemic model (2.4) obeys lim sup t 1 t log(i(t)) α 1π 1 + α 2 π 2 a.s. (2.8) By Proposition 2, we hence conclude that I(t) tends to zero exponentially almost surely. In other words, the disease dies out with probability one.
31 Outline Motivation 1 Motivation Prey-predator model Prey-predator model with Markovian switching SIS 2 3
32 Theorem If T0 S > 1, then, for any given initial value I 0 (0, N), the solution of the stochastic SIS model (2.4) has the properties that lim inf I(t) π 1α 1 + π 2 α 2 a.s. (2.9) t π 1 β 1 + π 2 β 2 and lim sup I(t) π 1α 1 + π 2 α 2 a.s. (2.10) t π 1 β 1 + π 2 β 2 In other words, the disease will reach the neighbourhood of the level π 1α 1 +π 2 α 2 π 1 β 1 +π 2 β 2 infinitely many times with probability one.
33 To reveal more properties of the stochastic SIS model, we observe from Proposition 2 that T0 S > 1 is equivalent to the condition that π 1 α 1 + π 2 α 2 > 0. This may be divided into two cases: (a) both α 1 and α 2 are positive; (b) only one of α 1 and α 2 is positive. Without loss of generality, we may assume that 0 < α 1 /β 1 = α 2 /β 2 or 0 < α 1 /β 1 < α 2 /β 2 in Case (a), while α 1 /β 1 0 < α 2 /β 2 in Case (b). So there are three different cases to be considered under condition T S 0 > 1.
34 Lemma The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then I(t) = α 1 /β 1 for all t > 0 when I 0 = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then I(t) (α 1 /β 1, α 2 /β 2 ) for all t > 0 whenever I 0 (α 1 /β 1, α 2 /β 2 ). (iii) If α 1 /β 1 0 < α 2 /β 2, then I(t) (0, α 2 /β 2 ) for all t > 0 whenever I 0 (0, α 2 /β 2 ).
35 Lemma The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then I(t) = α 1 /β 1 for all t > 0 when I 0 = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then I(t) (α 1 /β 1, α 2 /β 2 ) for all t > 0 whenever I 0 (α 1 /β 1, α 2 /β 2 ). (iii) If α 1 /β 1 0 < α 2 /β 2, then I(t) (0, α 2 /β 2 ) for all t > 0 whenever I 0 (0, α 2 /β 2 ).
36 Lemma The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then I(t) = α 1 /β 1 for all t > 0 when I 0 = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then I(t) (α 1 /β 1, α 2 /β 2 ) for all t > 0 whenever I 0 (α 1 /β 1, α 2 /β 2 ). (iii) If α 1 /β 1 0 < α 2 /β 2, then I(t) (0, α 2 /β 2 ) for all t > 0 whenever I 0 (0, α 2 /β 2 ).
37 Lemma The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then I(t) = α 1 /β 1 for all t > 0 when I 0 = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then I(t) (α 1 /β 1, α 2 /β 2 ) for all t > 0 whenever I 0 (α 1 /β 1, α 2 /β 2 ). (iii) If α 1 /β 1 0 < α 2 /β 2, then I(t) (0, α 2 /β 2 ) for all t > 0 whenever I 0 (0, α 2 /β 2 ).
38 Theorem Assume that T0 S > 1 and let I 0 (0, N) be arbitrary. The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then lim t I(t) = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then α 1 lim inf β 1 t (iii) If α 1 /β 1 0 < α 2 /β 2, then 0 lim inf t I(t) lim sup I(t) α 2. t β 2 I(t) lim sup I(t) α 2. t β 2
39 Theorem Assume that T0 S > 1 and let I 0 (0, N) be arbitrary. The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then lim t I(t) = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then α 1 lim inf β 1 t (iii) If α 1 /β 1 0 < α 2 /β 2, then 0 lim inf t I(t) lim sup I(t) α 2. t β 2 I(t) lim sup I(t) α 2. t β 2
40 Theorem Assume that T0 S > 1 and let I 0 (0, N) be arbitrary. The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then lim t I(t) = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then α 1 lim inf β 1 t (iii) If α 1 /β 1 0 < α 2 /β 2, then 0 lim inf t I(t) lim sup I(t) α 2. t β 2 I(t) lim sup I(t) α 2. t β 2
41 Theorem Assume that T0 S > 1 and let I 0 (0, N) be arbitrary. The following statements hold with probability one: (i) If 0 < α 1 /β 1 = α 2 /β 2, then lim t I(t) = α 1 /β 1. (ii) If 0 < α 1 /β 1 < α 2 /β 2, then α 1 lim inf β 1 t (iii) If α 1 /β 1 0 < α 2 /β 2, then 0 lim inf t I(t) lim sup I(t) α 2. t β 2 I(t) lim sup I(t) α 2. t β 2
42 Theorem Assume that T0 S > 1 and 0 < α 1 β 1 < α 2 β 2, and let I 0 (0, N) be arbitrary. Then for any ε > 0, sufficiently small for α 1 β 1 + ε < π 1α 1 + π 2 α 2 π 1 β 1 + π 2 β 2 < α 2 β 2 ε, the solution of the stochastic SIS epidemic model (2.4) has the properties that ( P lim inf I(t) < α ) 1 + ε e ν 12T 1 (ε), (2.11) t β 1 and ( P lim sup I(t) > α ) 2 ε e ν 21T 2 (ε), (2.12) t β 2 where T 1 (ε) > 0 and T 2 (ε) > 0 are defined by
43 T 1 (ε) = 1 ( ( β1 log β ) ( 2 α1 ) ( εβ1 )) +log +ε log α 1 α 1 α 2 β 1 α 1 (2.13) and T 2 (ε) = 1 ( ( β1 log β ) ( 2 α2 ) ( εβ2 )) +log ε log. (2.14) α 2 α 1 α 2 β 2 α 2
44 Theorem Assume that T0 S > 1 (namely π 1α 1 + π 2 α 2 > 0) and α 1 β 1 0 < α 2 β 2. Let I 0 (0, N) be arbitrary. Then for any ε > 0, sufficiently small for ε < π 1α 1 + π 2 α 2 π 1 β 1 + π 2 β 2 < α 2 β 2 ε, the solution of the stochastic SIS model (2.4) has the properties that ( ) P lim inf I(t) < ε e ν 12T 3 (ε), (2.15) t and ( P lim sup I(t) > α ) 2 ε e ν 21T 4 (ε), (2.16) t β 2 where T 3 (ε) > 0 and T 4 (ε) > 0 are defined by
45 T 3 (ε) = 1 ( ( β2 log β ) ( 1 +log ε α ) ( 1 α1 )) log ε α 1 α 2 α 1 β 1 β 1 (2.17) and T 4 (ε) = 1 ( ( 2 log α 2 ε β ) ( 2 α2 ) ( + log ε log ε β )) 2. (2.18) α 2 β 2 α 2
46 Use another pdf file.
Systems with Persistent Memory: the Observation Inequality Problems and Solutions
Chapter 6 Systems with Persistent Memory: the Observation Inequality Problems and Solutions Facts that are recalled in the problems wt) = ut) + 1 c A 1 s ] R c t s)) hws) + Ks r)wr)dr ds. 6.1) w = w +
More informationSome stability results of parameter identification in a jump diffusion model
Some stability results of parameter identification in a jump diffusion model D. Düvelmeyer Technische Universität Chemnitz, Fakultät für Mathematik, 09107 Chemnitz, Germany Abstract In this paper we discuss
More informationMathematical Finance
Mathematical Finance Option Pricing under the Risk-Neutral Measure Cory Barnes Department of Mathematics University of Washington June 11, 2013 Outline 1 Probability Background 2 Black Scholes for European
More informationNotes on metric spaces
Notes on metric spaces 1 Introduction The purpose of these notes is to quickly review some of the basic concepts from Real Analysis, Metric Spaces and some related results that will be used in this course.
More informationOnline Appendix to Stochastic Imitative Game Dynamics with Committed Agents
Online Appendix to Stochastic Imitative Game Dynamics with Committed Agents William H. Sandholm January 6, 22 O.. Imitative protocols, mean dynamics, and equilibrium selection In this section, we consider
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete
More informationStochastic Gene Expression in Prokaryotes: A Point Process Approach
Stochastic Gene Expression in Prokaryotes: A Point Process Approach Emanuele LEONCINI INRIA Rocquencourt - INRA Jouy-en-Josas Mathematical Modeling in Cell Biology March 27 th 2013 Emanuele LEONCINI (INRIA)
More informationLecture 6 Black-Scholes PDE
Lecture 6 Black-Scholes PDE Lecture Notes by Andrzej Palczewski Computational Finance p. 1 Pricing function Let the dynamics of underlining S t be given in the risk-neutral measure Q by If the contingent
More informationARBITRAGE-FREE OPTION PRICING MODELS. Denis Bell. University of North Florida
ARBITRAGE-FREE OPTION PRICING MODELS Denis Bell University of North Florida Modelling Stock Prices Example American Express In mathematical finance, it is customary to model a stock price by an (Ito) stochatic
More informationLecture 7: Finding Lyapunov Functions 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.243j (Fall 2003): DYNAMICS OF NONLINEAR SYSTEMS by A. Megretski Lecture 7: Finding Lyapunov Functions 1
More informationEXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL
EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL Exit Time problems and Escape from a Potential Well Escape From a Potential Well There are many systems in physics, chemistry and biology that exist
More informationNonparametric adaptive age replacement with a one-cycle criterion
Nonparametric adaptive age replacement with a one-cycle criterion P. Coolen-Schrijner, F.P.A. Coolen Department of Mathematical Sciences University of Durham, Durham, DH1 3LE, UK e-mail: Pauline.Schrijner@durham.ac.uk
More informationSensitivity analysis of utility based prices and risk-tolerance wealth processes
Sensitivity analysis of utility based prices and risk-tolerance wealth processes Dmitry Kramkov, Carnegie Mellon University Based on a paper with Mihai Sirbu from Columbia University Math Finance Seminar,
More informationON LIMIT LAWS FOR CENTRAL ORDER STATISTICS UNDER POWER NORMALIZATION. E. I. Pancheva, A. Gacovska-Barandovska
Pliska Stud. Math. Bulgar. 22 (2015), STUDIA MATHEMATICA BULGARICA ON LIMIT LAWS FOR CENTRAL ORDER STATISTICS UNDER POWER NORMALIZATION E. I. Pancheva, A. Gacovska-Barandovska Smirnov (1949) derived four
More informationConditional Tail Expectations for Multivariate Phase Type Distributions
Conditional Tail Expectations for Multivariate Phase Type Distributions Jun Cai Department of Statistics and Actuarial Science University of Waterloo Waterloo, ON N2L 3G1, Canada jcai@math.uwaterloo.ca
More informationStochastic Gene Expression in Prokaryotes: A Point Process Approach
Stochastic Gene Expression in Prokaryotes: A Point Process Approach Emanuele LEONCINI INRIA Rocquencourt - INRA Jouy-en-Josas ASMDA Mataró June 28 th 2013 Emanuele LEONCINI (INRIA) Stochastic Gene Expression
More informationMonte Carlo Methods in Finance
Author: Yiyang Yang Advisor: Pr. Xiaolin Li, Pr. Zari Rachev Department of Applied Mathematics and Statistics State University of New York at Stony Brook October 2, 2012 Outline Introduction 1 Introduction
More informationHow To Order Infection Rates Based On Degree Distribution In A Network
Relating Network Structure to Di usion Properties through Stochastic Dominance by Matthew O. Jackson and Brian W. Rogers Draft: December 15, 2006 Forthcoming in Advances in Economic Theory y Abstract We
More information1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let
Copyright c 2009 by Karl Sigman 1 Stopping Times 1.1 Stopping Times: Definition Given a stochastic process X = {X n : n 0}, a random time τ is a discrete random variable on the same probability space as
More informationAdaptive Search with Stochastic Acceptance Probabilities for Global Optimization
Adaptive Search with Stochastic Acceptance Probabilities for Global Optimization Archis Ghate a and Robert L. Smith b a Industrial Engineering, University of Washington, Box 352650, Seattle, Washington,
More informationDisability insurance: estimation and risk aggregation
Disability insurance: estimation and risk aggregation B. Löfdahl Department of Mathematics KTH, Royal Institute of Technology May 2015 Introduction New upcoming regulation for insurance industry: Solvency
More information1 if 1 x 0 1 if 0 x 1
Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or
More informationBisimulation and Logical Preservation for Continuous-Time Markov Decision Processes
Bisimulation and Logical Preservation for Continuous-Time Markov Decision Processes Martin R. Neuhäußer 1,2 Joost-Pieter Katoen 1,2 1 RWTH Aachen University, Germany 2 University of Twente, The Netherlands
More informationStatistics in Retail Finance. Chapter 6: Behavioural models
Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics:- Behavioural
More informationOptimal proportional reinsurance and dividend pay-out for insurance companies with switching reserves
Optimal proportional reinsurance and dividend pay-out for insurance companies with switching reserves Abstract: This paper presents a model for an insurance company that controls its risk and dividend
More informationHydrodynamic Limits of Randomized Load Balancing Networks
Hydrodynamic Limits of Randomized Load Balancing Networks Kavita Ramanan and Mohammadreza Aghajani Brown University Stochastic Networks and Stochastic Geometry a conference in honour of François Baccelli
More informationBuilding a Smooth Yield Curve. University of Chicago. Jeff Greco
Building a Smooth Yield Curve University of Chicago Jeff Greco email: jgreco@math.uchicago.edu Preliminaries As before, we will use continuously compounding Act/365 rates for both the zero coupon rates
More informationFinancial TIme Series Analysis: Part II
Department of Mathematics and Statistics, University of Vaasa, Finland January 29 February 13, 2015 Feb 14, 2015 1 Univariate linear stochastic models: further topics Unobserved component model Signal
More informationOPTIMAL CONTROL OF FLEXIBLE SERVERS IN TWO TANDEM QUEUES WITH OPERATING COSTS
Probability in the Engineering and Informational Sciences, 22, 2008, 107 131. Printed in the U.S.A. DOI: 10.1017/S0269964808000077 OPTIMAL CONTROL OF FLEXILE SERVERS IN TWO TANDEM QUEUES WITH OPERATING
More informationCOMPLETE MARKETS DO NOT ALLOW FREE CASH FLOW STREAMS
COMPLETE MARKETS DO NOT ALLOW FREE CASH FLOW STREAMS NICOLE BÄUERLE AND STEFANIE GRETHER Abstract. In this short note we prove a conjecture posed in Cui et al. 2012): Dynamic mean-variance problems in
More informationOptimization under uncertainty: modeling and solution methods
Optimization under uncertainty: modeling and solution methods Paolo Brandimarte Dipartimento di Scienze Matematiche Politecnico di Torino e-mail: paolo.brandimarte@polito.it URL: http://staff.polito.it/paolo.brandimarte
More information1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since
More informationA NOTE ON THE EFFECTS OF TAXES AND TRANSACTION COSTS ON OPTIMAL INVESTMENT. October 27, 2005. Abstract
A NOTE ON THE EFFECTS OF TAXES AND TRANSACTION COSTS ON OPTIMAL INVESTMENT Cristin Buescu 1, Abel Cadenillas 2 and Stanley R Pliska 3 October 27, 2005 Abstract We integrate two approaches to portfolio
More informationFactoring & Primality
Factoring & Primality Lecturer: Dimitris Papadopoulos In this lecture we will discuss the problem of integer factorization and primality testing, two problems that have been the focus of a great amount
More informationFluid Approximation of Smart Grid Systems: Optimal Control of Energy Storage Units
Fluid Approximation of Smart Grid Systems: Optimal Control of Energy Storage Units by Rasha Ibrahim Sakr, B.Sc. A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment
More informationThe Ergodic Theorem and randomness
The Ergodic Theorem and randomness Peter Gács Department of Computer Science Boston University March 19, 2008 Peter Gács (Boston University) Ergodic theorem March 19, 2008 1 / 27 Introduction Introduction
More informationFurther Study on Strong Lagrangian Duality Property for Invex Programs via Penalty Functions 1
Further Study on Strong Lagrangian Duality Property for Invex Programs via Penalty Functions 1 J. Zhang Institute of Applied Mathematics, Chongqing University of Posts and Telecommunications, Chongqing
More informationDynamic Assignment of Dedicated and Flexible Servers in Tandem Lines
Dynamic Assignment of Dedicated and Flexible Servers in Tandem Lines Sigrún Andradóttir and Hayriye Ayhan School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332-0205,
More informationSimple Arbitrage. Motivated by and partly based on a joint work with T. Sottinen and E. Valkeila. Christian Bender. Saarland University
Simple Arbitrage Motivated by and partly based on a joint work with T. Sottinen and E. Valkeila Saarland University December, 8, 2011 Problem Setting Financial market with two assets (for simplicity) on
More informationDuality of linear conic problems
Duality of linear conic problems Alexander Shapiro and Arkadi Nemirovski Abstract It is well known that the optimal values of a linear programming problem and its dual are equal to each other if at least
More informationTHE CENTRAL LIMIT THEOREM TORONTO
THE CENTRAL LIMIT THEOREM DANIEL RÜDT UNIVERSITY OF TORONTO MARCH, 2010 Contents 1 Introduction 1 2 Mathematical Background 3 3 The Central Limit Theorem 4 4 Examples 4 4.1 Roulette......................................
More informationTutorial: Stochastic Modeling in Biology Applications of Discrete- Time Markov Chains. NIMBioS Knoxville, Tennessee March 16-18, 2011
Tutorial: Stochastic Modeling in Biology Applications of Discrete- Time Markov Chains Linda J. S. Allen Texas Tech University Lubbock, Texas U.S.A. NIMBioS Knoxville, Tennessee March 16-18, 2011 OUTLINE
More informationVALUATION OF STOCK LOANS WITH REGIME SWITCHING
VALUATION OF STOCK LOANS WITH REGIME SWITCHING QING ZHANG AND XUN YU ZHOU Abstract. This paper is concerned with stock loan valuation in which the underlying stock price is dictated by geometric Brownian
More informationLS.6 Solution Matrices
LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions
More informationA Decomposition Approach for a Capacitated, Single Stage, Production-Inventory System
A Decomposition Approach for a Capacitated, Single Stage, Production-Inventory System Ganesh Janakiraman 1 IOMS-OM Group Stern School of Business New York University 44 W. 4th Street, Room 8-160 New York,
More informationChapter 2: Binomial Methods and the Black-Scholes Formula
Chapter 2: Binomial Methods and the Black-Scholes Formula 2.1 Binomial Trees We consider a financial market consisting of a bond B t = B(t), a stock S t = S(t), and a call-option C t = C(t), where the
More informationREQUIREMENTS ON WORM MITIGATION TECHNOLOGIES IN MANETS
REQUIREMENTS ON WORM MITIGATION TECHNOLOGIES IN MANETS Robert G. Cole and Nam Phamdo JHU Applied Physics Laboratory {robert.cole,nam.phamdo}@jhuapl.edu Moheeb A. Rajab and Andreas Terzis Johns Hopkins
More informationA PRIORI ESTIMATES FOR SEMISTABLE SOLUTIONS OF SEMILINEAR ELLIPTIC EQUATIONS. In memory of Rou-Huai Wang
A PRIORI ESTIMATES FOR SEMISTABLE SOLUTIONS OF SEMILINEAR ELLIPTIC EQUATIONS XAVIER CABRÉ, MANEL SANCHÓN, AND JOEL SPRUCK In memory of Rou-Huai Wang 1. Introduction In this note we consider semistable
More information4: SINGLE-PERIOD MARKET MODELS
4: SINGLE-PERIOD MARKET MODELS Ben Goldys and Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2015 B. Goldys and M. Rutkowski (USydney) Slides 4: Single-Period Market
More informationA Uniform Asymptotic Estimate for Discounted Aggregate Claims with Subexponential Tails
12th International Congress on Insurance: Mathematics and Economics July 16-18, 2008 A Uniform Asymptotic Estimate for Discounted Aggregate Claims with Subexponential Tails XUEMIAO HAO (Based on a joint
More informationMATH 590: Meshfree Methods
MATH 590: Meshfree Methods Chapter 7: Conditionally Positive Definite Functions Greg Fasshauer Department of Applied Mathematics Illinois Institute of Technology Fall 2010 fasshauer@iit.edu MATH 590 Chapter
More informationLecture. S t = S t δ[s t ].
Lecture In real life the vast majority of all traded options are written on stocks having at least one dividend left before the date of expiration of the option. Thus the study of dividends is important
More informationWhen Promotions Meet Operations: Cross-Selling and Its Effect on Call-Center Performance
When Promotions Meet Operations: Cross-Selling and Its Effect on Call-Center Performance Mor Armony 1 Itay Gurvich 2 July 27, 2006 Abstract We study cross-selling operations in call centers. The following
More informationBANACH AND HILBERT SPACE REVIEW
BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but
More informationSTOCK LOANS. XUN YU ZHOU Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong 1.
Mathematical Finance, Vol. 17, No. 2 April 2007), 307 317 STOCK LOANS JIANMING XIA Center for Financial Engineering and Risk Management, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
More informationSection 4.2: The Division Algorithm and Greatest Common Divisors
Section 4.2: The Division Algorithm and Greatest Common Divisors The Division Algorithm The Division Algorithm is merely long division restated as an equation. For example, the division 29 r. 20 32 948
More informationJung-Soon Hyun and Young-Hee Kim
J. Korean Math. Soc. 43 (2006), No. 4, pp. 845 858 TWO APPROACHES FOR STOCHASTIC INTEREST RATE OPTION MODEL Jung-Soon Hyun and Young-Hee Kim Abstract. We present two approaches of the stochastic interest
More informationUNIFORM ASYMPTOTICS FOR DISCOUNTED AGGREGATE CLAIMS IN DEPENDENT RISK MODELS
Applied Probability Trust 2 October 2013 UNIFORM ASYMPTOTICS FOR DISCOUNTED AGGREGATE CLAIMS IN DEPENDENT RISK MODELS YANG YANG, Nanjing Audit University, and Southeast University KAIYONG WANG, Southeast
More information15 Limit sets. Lyapunov functions
15 Limit sets. Lyapunov functions At this point, considering the solutions to ẋ = f(x), x U R 2, (1) we were most interested in the behavior of solutions when t (sometimes, this is called asymptotic behavior
More informationFuzzy 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 informationCONTROLLABILITY. Chapter 2. 2.1 Reachable Set and Controllability. Suppose we have a linear system described by the state equation
Chapter 2 CONTROLLABILITY 2 Reachable Set and Controllability Suppose we have a linear system described by the state equation ẋ Ax + Bu (2) x() x Consider the following problem For a given vector x in
More informationRate of convergence towards Hartree dynamics
Rate of convergence towards Hartree dynamics Benjamin Schlein, LMU München and University of Cambridge Universitá di Milano Bicocca, October 22, 2007 Joint work with I. Rodnianski 1. Introduction boson
More informationAdaptive Online Gradient Descent
Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650
More informationFACTORING POLYNOMIALS IN THE RING OF FORMAL POWER SERIES OVER Z
FACTORING POLYNOMIALS IN THE RING OF FORMAL POWER SERIES OVER Z DANIEL BIRMAJER, JUAN B GIL, AND MICHAEL WEINER Abstract We consider polynomials with integer coefficients and discuss their factorization
More informationNOTES ON LINEAR TRANSFORMATIONS
NOTES ON LINEAR TRANSFORMATIONS Definition 1. Let V and W be vector spaces. A function T : V W is a linear transformation from V to W if the following two properties hold. i T v + v = T v + T v for all
More informationTD(0) Leads to Better Policies than Approximate Value Iteration
TD(0) Leads to Better Policies than Approximate Value Iteration Benjamin Van Roy Management Science and Engineering and Electrical Engineering Stanford University Stanford, CA 94305 bvr@stanford.edu Abstract
More informationConstant Elasticity of Variance (CEV) Option Pricing Model:Integration and Detailed Derivation
Constant Elasticity of Variance (CEV) Option Pricing Model:Integration and Detailed Derivation Ying-Lin Hsu Department of Applied Mathematics National Chung Hsing University Co-authors: T. I. Lin and C.
More informationThe Heat Equation. Lectures INF2320 p. 1/88
The Heat Equation Lectures INF232 p. 1/88 Lectures INF232 p. 2/88 The Heat Equation We study the heat equation: u t = u xx for x (,1), t >, (1) u(,t) = u(1,t) = for t >, (2) u(x,) = f(x) for x (,1), (3)
More informationUsing Real Data in an SIR Model
Using Real Data in an SIR Model D. Sulsky June 21, 2012 In most epidemics it is difficult to determine how many new infectives there are each day since only those that are removed, for medical aid or other
More informationHow To Optimize Email Traffic
Adaptive Threshold Policies for Multi-Channel Call Centers Benjamin Legros a Oualid Jouini a Ger Koole b a Laboratoire Génie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290 Châtenay-Malabry,
More informationOPTIMAL TIMING OF THE ANNUITY PURCHASES: A
OPTIMAL TIMING OF THE ANNUITY PURCHASES: A COMBINED STOCHASTIC CONTROL AND OPTIMAL STOPPING PROBLEM Gabriele Stabile 1 1 Dipartimento di Matematica per le Dec. Econ. Finanz. e Assic., Facoltà di Economia
More informationOn Load Balancing in Erlang Networks. The dynamic resource allocation problem arises in a variety of applications.
1 On Load Balancing in Erlang Networks 1.1 Introduction The dynamic resource allocation problem arises in a variety of applications. The generic resource allocation setting involves a number of locations
More informationEinführung in die Mathematische Epidemiologie: Introduction to Mathematical Epidemiology: Deterministic Compartmental Models
Einführung in die Mathematische Epidemiologie: Introduction to Mathematical Epidemiology: Deterministic Compartmental Models Nakul Chitnis Universität Basel Mathematisches Institut Swiss Tropical and Public
More informationSystems of Linear Equations
Systems of Linear Equations Beifang Chen Systems of linear equations Linear systems A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where a, a,, a n and
More informationOnline Appendix. Supplemental Material for Insider Trading, Stochastic Liquidity and. Equilibrium Prices. by Pierre Collin-Dufresne and Vyacheslav Fos
Online Appendix Supplemental Material for Insider Trading, Stochastic Liquidity and Equilibrium Prices by Pierre Collin-Dufresne and Vyacheslav Fos 1. Deterministic growth rate of noise trader volatility
More informationUniversal Algorithm for Trading in Stock Market Based on the Method of Calibration
Universal Algorithm for Trading in Stock Market Based on the Method of Calibration Vladimir V yugin Institute for Information Transmission Problems, Russian Academy of Sciences, Bol shoi Karetnyi per.
More informationLecture 15. Sergei Fedotov. 20912 - Introduction to Financial Mathematics. Sergei Fedotov (University of Manchester) 20912 2010 1 / 6
Lecture 15 Sergei Fedotov 20912 - Introduction to Financial Mathematics Sergei Fedotov (University of Manchester) 20912 2010 1 / 6 Lecture 15 1 Black-Scholes Equation and Replicating Portfolio 2 Static
More informationStocks paying discrete dividends: modelling and option pricing
Stocks paying discrete dividends: modelling and option pricing Ralf Korn 1 and L. C. G. Rogers 2 Abstract In the Black-Scholes model, any dividends on stocks are paid continuously, but in reality dividends
More informationContinued Fractions and the Euclidean Algorithm
Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction
More informationThe Exponential Distribution
21 The Exponential Distribution From Discrete-Time to Continuous-Time: In Chapter 6 of the text we will be considering Markov processes in continuous time. In a sense, we already have a very good understanding
More informationSome Research Problems in Uncertainty Theory
Journal of Uncertain Systems Vol.3, No.1, pp.3-10, 2009 Online at: www.jus.org.uk Some Research Problems in Uncertainty Theory aoding Liu Uncertainty Theory Laboratory, Department of Mathematical Sciences
More informationSTUDIES OF INVENTORY CONTROL AND CAPACITY PLANNING WITH MULTIPLE SOURCES. A Dissertation Presented to The Academic Faculty. Frederick Craig Zahrn
STUDIES OF INVENTORY CONTROL AND CAPACITY PLANNING WITH MULTIPLE SOURCES A Dissertation Presented to The Academic Faculty By Frederick Craig Zahrn In Partial Fulfillment Of the Requirements for the Degree
More information5. Factoring by the QF method
5. Factoring by the QF method 5.0 Preliminaries 5.1 The QF view of factorability 5.2 Illustration of the QF view of factorability 5.3 The QF approach to factorization 5.4 Alternative factorization by the
More informationThe Joint Distribution of Server State and Queue Length of M/M/1/1 Retrial Queue with Abandonment and Feedback
The Joint Distribution of Server State and Queue Length of M/M/1/1 Retrial Queue with Abandonment and Feedback Hamada Alshaer Université Pierre et Marie Curie - Lip 6 7515 Paris, France Hamada.alshaer@lip6.fr
More informationFUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES
FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES CHRISTOPHER HEIL 1. Cosets and the Quotient Space Any vector space is an abelian group under the operation of vector addition. So, if you are have studied
More informationDoes Black-Scholes framework for Option Pricing use Constant Volatilities and Interest Rates? New Solution for a New Problem
Does Black-Scholes framework for Option Pricing use Constant Volatilities and Interest Rates? New Solution for a New Problem Gagan Deep Singh Assistant Vice President Genpact Smart Decision Services Financial
More informationStudy of Virus Propagation Model Under the Cloud
Tongrang Fan, Yanjing Li, Feng Gao School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 543, China Fantr29@26.com, 532465444 @qq.com, f.gao@live.com bstract. The
More information4 Lyapunov Stability Theory
4 Lyapunov Stability Theory In this section we review the tools of Lyapunov stability theory. These tools will be used in the next section to analyze the stability properties of a robot controller. We
More informationA note on Dulac functions for a sum of vector fields
AVANZA. Vol. iii. Fm - Iit, Uacj (2013) 17 23. Isbn: 978-607-9224-52-3 A note on Dulac functions for a sum of vector fields Osuna, Osvaldo, Vargas-De-León, Cruz, Villaseñor, Gabriel Abstract In this paper
More informationThe Black-Scholes pricing formulas
The Black-Scholes pricing formulas Moty Katzman September 19, 2014 The Black-Scholes differential equation Aim: Find a formula for the price of European options on stock. Lemma 6.1: Assume that a stock
More informationResearch Article Stability Analysis of an HIV/AIDS Dynamics Model with Drug Resistance
Discrete Dynamics in Nature and Society Volume 212, Article ID 162527, 13 pages doi:1.1155/212/162527 Research Article Stability Analysis of an HIV/AIDS Dynamics Model with Drug Resistance Qianqian Li,
More informationPayment streams and variable interest rates
Chapter 4 Payment streams and variable interest rates In this chapter we consider two extensions of the theory Firstly, we look at payment streams A payment stream is a payment that occurs continuously,
More informationProperties of BMO functions whose reciprocals are also BMO
Properties of BMO functions whose reciprocals are also BMO R. L. Johnson and C. J. Neugebauer The main result says that a non-negative BMO-function w, whose reciprocal is also in BMO, belongs to p> A p,and
More informationHedging bounded claims with bounded outcomes
Hedging bounded claims with bounded outcomes Freddy Delbaen ETH Zürich, Department of Mathematics, CH-892 Zurich, Switzerland Abstract. We consider a financial market with two or more separate components
More informationUndergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics
Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics An Introductory Single Variable Real Analysis: A Learning Approach through Problem Solving Marcel B. Finan c All Rights
More informationF. ABTAHI and M. ZARRIN. (Communicated by J. Goldstein)
Journal of Algerian Mathematical Society Vol. 1, pp. 1 6 1 CONCERNING THE l p -CONJECTURE FOR DISCRETE SEMIGROUPS F. ABTAHI and M. ZARRIN (Communicated by J. Goldstein) Abstract. For 2 < p
More informationMARTINGALES AND GAMBLING Louis H. Y. Chen Department of Mathematics National University of Singapore
MARTINGALES AND GAMBLING Louis H. Y. Chen Department of Mathematics National University of Singapore 1. Introduction The word martingale refers to a strap fastened belween the girth and the noseband of
More informationCHAPTER IV - BROWNIAN MOTION
CHAPTER IV - BROWNIAN MOTION JOSEPH G. CONLON 1. Construction of Brownian Motion There are two ways in which the idea of a Markov chain on a discrete state space can be generalized: (1) The discrete time
More informationIEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 50, NO. 5, MAY 2005 577. Least Mean Square Algorithms With Markov Regime-Switching Limit
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 50, NO. 5, MAY 2005 577 Least Mean Square Algorithms With Markov Regime-Switching Limit G. George Yin, Fellow, IEEE, and Vikram Krishnamurthy, Fellow, IEEE
More informationECON20310 LECTURE SYNOPSIS REAL BUSINESS CYCLE
ECON20310 LECTURE SYNOPSIS REAL BUSINESS CYCLE YUAN TIAN This synopsis is designed merely for keep a record of the materials covered in lectures. Please refer to your own lecture notes for all proofs.
More information