Modeling with Itô Stochastic Differential Equations
|
|
- Elwin Cole
- 6 years ago
- Views:
Transcription
1 Modeling with Itô Stochastic Differential Equations E. Allen presentation by T. Perälä Postgraduate seminar on applied mathematics 2009
2 Outline Hilbert Space of Stochastic Processes ( 2.4) Computer Generation of Stochastic Processes ( 2.) Examples of Stochastic Processes ( 2.6)
3 2.4 A Hilbert Space of Stochastic Processes Motivation: Hilbert space is needed for discussing stochastic integrals and stochastic differential equations For example, we can show convergence of a sequence of stochastic processes in Hilbert space. In particular, Cauchy sequences in the Hilbert space will converge in the space. In this part of the talk, we first describe a metric space consisting of elementary stochastic processes. This space will then be completed to a Hilbert space and the set of elementary stochastic processes will be dense in the Hilbert space
4 Metric Space of Elementary Stochastic Processes Consider continuous stochastic processes defined on the interval and probability space. Let be an elementary stochastic process which is a random step function defined on. That is, it is of form where is a partition of and is the characteristic function. Random step function on [0,], Gaussian white noise 2 0!! It is assumed that the random variable for each, in particular, for each. Now, the metric space is defined as
5 Metric Space of Elementary Stochastic Processes On, the inner product is defined as and the norm as Compare to inner product in, which was, and the norm. The space is a metric space with the metric. However, is not complete since not all Cauchy sequences converge in. This space can be completed by adding to it additional stochastic processes. The complete space is denoted as and is dense in. That is, given and given there is a such that.
6 Some Useful Results Suppose, for example, that a stochastic process satisfies, for some positive constants and, the inequalities and Then, and forms a Cauchy sequence in that converges to. Indeed the norm is bounded, In addition, Fubini s theorem (F) states that For, the Cauchy-Schwarz inequality (C-S),, is very useful and written explicitly has the form Thus, for example, applying C-S and F Furthermore, the triangle inequality, is explicitly
7 Simple Random Step Functions Sometimes it is useful to apply even more elementary set of stochastic processes than those in. Let be the set of simple random step functions. That is, has the form As simple functions are dense in, is also dense in. Next we will have some examples to clarify Hilbert space of stochastic processes emphasis:. But before that, for
8 Example 2.8. A Converging Seq. of Stochastic Processes Define the stochastic process as and is a Wiener process on. Clearly. Also, Since, the sequence of stochastic processes converges to in.
9 Example 2.9. Another Converging Seq. of Stochastic Proc s Let be a Wiener process on. Define the stochastic process in the following way: Then Thus, converges to in as.
10 Example 2.0. Integration of a Function of a Poisson Proc. Consider where is a Poisson process with intensity on the interval. Suppose that experiences unit increases at the times on and let and. Then can be written in the form Then, it is easily seen that Furthermore, it is interesting that Also, as, then and.
11 Example 2.. A Commonly Used Approx. to Wiener Proc. Consider the interval and let where. Let be a Wiener process. Define the continuous piecewise linear stochastic process on this partition of by for and. Notice that for and is continuous on. Also, Thus,, i.e.,.
12 Example 2.. A Commonly Used Approx. to Wiener Proc. For a large value of, the graph of a sample path of corresponding sample path of. is indistinguishable from the graph of the The graph of a Wiener process trajectory is often represented by plotting for a large value of. In the figure, two sample paths is used to generate the 000 values are plotted where the recurrence relation η i N(0, )
13 Some Properties of Stochastic Processes in Hilbert Space First, if, then which implies that for almost every. In addition, if converges to a stochastic process, then. Hence, for almost every. That is, convergence in Hilbert space implies convergence in probability on. Specifically, if for almost every. then for any Now, suppose that and satisfies for any for a constant. Then a bound on can be found and is continuous on with respect to the norm. First, if, then and Thus, is bounded by. Also, it is easy to see that is continuous on with respect to the norm. Given, then whenever.
14 Some Properties of Stochastic Processes in Hilbert Space Finally, it is useful to present some terminology used in the literature regarding the Hilbert space applied later in this seminar. that is Let be a Wiener process defined on a probability space. Let be a family of sub- -algebras of satisfying if, is -measurable, and is independent of. is the -algebra of events generated by the values of the Wiener process until time. A stochastic process is said to be adapted to if is independent of a Wiener increment Furthermore, the Hilbert space is the set of nonanticipative stochastic processes such that satisfies.
15 2. Computer Generation of Stochastic Processes Let s consider how stochastic processes can be computationally simulated using pseudo-random numbers. First, consider simulation of a nonhomogeneous Markov chain (MC) on where and is a discrete random variable for each time. Specifically,. The time dependent transition probability matrix is Consider generation of one trajectory or sample path. At time,. To find, are first computed for. Next, a pseudo-random number uniformly distributed on is generated.. Then, is calculated so that Finally, is set equal to. To find, are computed for. Then, uniformly distributed on is generated and is calculated so that Then is set equal to. These steps are repeated times to give on realization of the discrete stochastic process.
16 Example of Computer Generated Realization of MC Two homogenous Markov chains, !2! !2! 0 2 P = P = M = { 2,, 0,, 2}
17 Continuous Case Now consider generating a trajectory for a continuous Markov process. Generally, trajectories of continuous processes are determined at a discrete set of times. Specifically, a trajectory is calculated at the times where. Then, may be approximated between these points using, for example, piecewise linear approximation. Next some examples that illustrate this behavior are presented.
18 Example 2.2. Simulation of a Poisson Process Consider a Poisson process with intensity. Recall that equals the number of observations in time where the probability of one observation in time is equal to. From Example.4, we remember that Consider now simulating this continuous stochastic process at the discrete times where. Let and the random numbers are chosen so that Then, are Poisson distributed with intensity at the discrete times. Notice that to find given uniformly distributed on, one uses the relation
19 Simulation of a Poisson Process with Matlab Poisson process, h = λ = λ =
20 Example 2.3. Simulation of a Wiener Proc. Sample Path Consider the Wiener process on. Consider simulating this continuous stochastic process at the discrete times where. Let where and are normally distributed numbers with mean and variance. As in the previous example, each sample path of the continuous stochastic process is computed at the discrete times. Thus, To estimate, at a time for any, a continuous linear interpolant can be used as was shown in Example 2.. In particular,
21 Computer Generation of Wiener Process Sample Path Wiener process on [0, 0] N = 0 0 0!!0!! N = N = !!!0! !
22 Example 2.4. Simulation of Wiener by a Discrete Process Let. Define the discrete stochastic process on the partition in the following way. Let and let the transition probabilities be assuming that. Then, as explained in 2.2, the probability distribution for satisfies the forward Kolmogorov equations where. For small, approximately equals where satisfies the partial differential equation Solving this partial differential equation gives In particular, for, is approximately normally distributed with mean and variance. Furthermore, is approximately normally distributed with mean and variance and is independent of. Indeed, approximates a Wiener process on the partition.
23 2.6 Examples of Stochastic Processes Stochastic processes are common in physics, meteorology, and finance. Stochastic process occur whenever dynamical systems experience random (uncertain) influences A classical example is radioactive decay where atoms of unstable isotopes transform to other isotopes. Suppose that there are initially atoms of a radioactive isotope. Let be the number of atoms at time. Let be the decay constant of the isotope. This means that the probability that an atom transforms in small time interval is equal to. Consider finding the expected number of atoms at time, i.e.,. Let be the probability that there are atoms at time. Then, considering the possible transitions in time interval, one obtains Thus, letting,
24 Radioactive Decay Continued The expected number of atoms can now be computed as This leads to Hence, And as the solution to this differential equation we obtain for the expected number of atoms at time
25 Population Biology Population biology is rich in stochastic processes. The birth-death process, in itself, is a random process. Also, variability in the environment introduces additional random influences which are time and spatial varying. As a result, growth of a population exhibits random behavior.
26 Weather Climatic quantities can be considered stochastic processes Too many different influences to make an accurate deterministic model Annual precipitation in Lubbock, Texas exhibits a Wiener-like behavior
27 Stock Prices Stock trading involves many uncertainties For example stock prices are thus modeled as a stochastic process
PROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 218. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More information9 Brownian Motion: Construction
9 Brownian Motion: Construction 9.1 Definition and Heuristics The central limit theorem states that the standard Gaussian distribution arises as the weak limit of the rescaled partial sums S n / p n of
More informationSequential Monte Carlo methods for filtering of unobservable components of multidimensional diffusion Markov processes
Sequential Monte Carlo methods for filtering of unobservable components of multidimensional diffusion Markov processes Ellida M. Khazen * 13395 Coppermine Rd. Apartment 410 Herndon VA 20171 USA Abstract
More informationElementary Applications of Probability Theory
Elementary Applications of Probability Theory With an introduction to stochastic differential equations Second edition Henry C. Tuckwell Senior Research Fellow Stochastic Analysis Group of the Centre for
More informationHandbook of Stochastic Methods
C. W. Gardiner Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences Third Edition With 30 Figures Springer Contents 1. A Historical Introduction 1 1.1 Motivation I 1.2 Some Historical
More informationStochastic Partial Differential Equations with Levy Noise
Stochastic Partial Differential Equations with Levy Noise An Evolution Equation Approach S..PESZAT and J. ZABCZYK Institute of Mathematics, Polish Academy of Sciences' CAMBRIDGE UNIVERSITY PRESS Contents
More informationIn terms of measures: Exercise 1. Existence of a Gaussian process: Theorem 2. Remark 3.
1. GAUSSIAN PROCESSES A Gaussian process on a set T is a collection of random variables X =(X t ) t T on a common probability space such that for any n 1 and any t 1,...,t n T, the vector (X(t 1 ),...,X(t
More informationFinite-Horizon Statistics for Markov chains
Analyzing FSDT Markov chains Friday, September 30, 2011 2:03 PM Simulating FSDT Markov chains, as we have said is very straightforward, either by using probability transition matrix or stochastic update
More informationApplied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB Contents Getting Started Creating Arrays Mathematical Operations with Arrays Using Script Files and Managing Data Two-Dimensional Plots Programming in
More informationADVANCED ENGINEERING MATHEMATICS MATLAB
ADVANCED ENGINEERING MATHEMATICS WITH MATLAB THIRD EDITION Dean G. Duffy Contents Dedication Contents Acknowledgments Author Introduction List of Definitions Chapter 1: Complex Variables 1.1 Complex Numbers
More informationApplied Linear Algebra
Applied Linear Algebra Peter J. Olver School of Mathematics University of Minnesota Minneapolis, MN 55455 olver@math.umn.edu http://www.math.umn.edu/ olver Chehrzad Shakiban Department of Mathematics University
More informationFunctional Analysis Review
Outline 9.520: Statistical Learning Theory and Applications February 8, 2010 Outline 1 2 3 4 Vector Space Outline A vector space is a set V with binary operations +: V V V and : R V V such that for all
More informationcovariance function, 174 probability structure of; Yule-Walker equations, 174 Moving average process, fluctuations, 5-6, 175 probability structure of
Index* The Statistical Analysis of Time Series by T. W. Anderson Copyright 1971 John Wiley & Sons, Inc. Aliasing, 387-388 Autoregressive {continued) Amplitude, 4, 94 case of first-order, 174 Associated
More informationhttp://www.math.uah.edu/stat/markov/.xhtml 1 of 9 7/16/2009 7:20 AM Virtual Laboratories > 16. Markov Chains > 1 2 3 4 5 6 7 8 9 10 11 12 1. A Markov process is a random process in which the future is
More informationOrthonormal Bases Fall Consider an inner product space V with inner product f, g and norm
8.03 Fall 203 Orthonormal Bases Consider an inner product space V with inner product f, g and norm f 2 = f, f Proposition (Continuity) If u n u 0 and v n v 0 as n, then u n u ; u n, v n u, v. Proof. Note
More informationHandbook of Stochastic Methods
Springer Series in Synergetics 13 Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences von Crispin W Gardiner Neuausgabe Handbook of Stochastic Methods Gardiner schnell und portofrei
More informationLinear Algebra I for Science (NYC)
Element No. 1: To express concrete problems as linear equations. To solve systems of linear equations using matrices. Topic: MATRICES 1.1 Give the definition of a matrix, identify the elements and the
More informationCourses: Mathematics (MATH)College: Natural Sciences & Mathematics. Any TCCN equivalents are indicated in square brackets [ ].
Courses: Mathematics (MATH)College: Natural Sciences & Mathematics Any TCCN equivalents are indicated in square brackets [ ]. MATH 1300: Fundamentals of Mathematics Cr. 3. (3-0). A survey of precollege
More informationApplied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB Contents Getting Started Creating Arrays Mathematical Operations with Arrays Using Script Files and Managing Data Two-Dimensional Plots Programming in
More informationContents. 1 Preliminaries 3. Martingales
Table of Preface PART I THE FUNDAMENTAL PRINCIPLES page xv 1 Preliminaries 3 2 Martingales 9 2.1 Martingales and examples 9 2.2 Stopping times 12 2.3 The maximum inequality 13 2.4 Doob s inequality 14
More informationGAUSSIAN PROCESSES; KOLMOGOROV-CHENTSOV THEOREM
GAUSSIAN PROCESSES; KOLMOGOROV-CHENTSOV THEOREM STEVEN P. LALLEY 1. GAUSSIAN PROCESSES: DEFINITIONS AND EXAMPLES Definition 1.1. A standard (one-dimensional) Wiener process (also called Brownian motion)
More informationThe Codimension of the Zeros of a Stable Process in Random Scenery
The Codimension of the Zeros of a Stable Process in Random Scenery Davar Khoshnevisan The University of Utah, Department of Mathematics Salt Lake City, UT 84105 0090, U.S.A. davar@math.utah.edu http://www.math.utah.edu/~davar
More informationMeasure, Integration & Real Analysis
v Measure, Integration & Real Analysis preliminary edition 10 August 2018 Sheldon Axler Dedicated to Paul Halmos, Don Sarason, and Allen Shields, the three mathematicians who most helped me become a mathematician.
More informationBrownian motion. Samy Tindel. Purdue University. Probability Theory 2 - MA 539
Brownian motion Samy Tindel Purdue University Probability Theory 2 - MA 539 Mostly taken from Brownian Motion and Stochastic Calculus by I. Karatzas and S. Shreve Samy T. Brownian motion Probability Theory
More informationPROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 15. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More informationIntroduction to the Mathematics of Medical Imaging
Introduction to the Mathematics of Medical Imaging Second Edition Charles L. Epstein University of Pennsylvania Philadelphia, Pennsylvania EiaJTL Society for Industrial and Applied Mathematics Philadelphia
More informationAlso, in recent years, Tsallis proposed another entropy measure which in the case of a discrete random variable is given by
Gibbs-Shannon Entropy and Related Measures: Tsallis Entropy Garimella Rama Murthy, Associate Professor, IIIT---Hyderabad, Gachibowli, HYDERABAD-32, AP, INDIA ABSTRACT In this research paper, it is proved
More informationLecture 19 L 2 -Stochastic integration
Lecture 19: L 2 -Stochastic integration 1 of 12 Course: Theory of Probability II Term: Spring 215 Instructor: Gordan Zitkovic Lecture 19 L 2 -Stochastic integration The stochastic integral for processes
More informationWhat s more chaotic than chaos itself? Brownian Motion - before, after, and beyond.
Include Only If Paper Has a Subtitle Department of Mathematics and Statistics What s more chaotic than chaos itself? Brownian Motion - before, after, and beyond. Math Graduate Seminar March 2, 2011 Outline
More informationStochastic Processes and Advanced Mathematical Finance. Stochastic Processes
Steven R. Dunbar Department of Mathematics 203 Avery Hall University of Nebraska-Lincoln Lincoln, NE 68588-0130 http://www.math.unl.edu Voice: 402-472-3731 Fax: 402-472-8466 Stochastic Processes and Advanced
More informationResearch Article A Necessary Characteristic Equation of Diffusion Processes Having Gaussian Marginals
Abstract and Applied Analysis Volume 01, Article ID 598590, 9 pages doi:10.1155/01/598590 Research Article A Necessary Characteristic Equation of Diffusion Processes Having Gaussian Marginals Syeda Rabab
More informationGaussian Processes. 1. Basic Notions
Gaussian Processes 1. Basic Notions Let T be a set, and X : {X } T a stochastic process, defined on a suitable probability space (Ω P), that is indexed by T. Definition 1.1. We say that X is a Gaussian
More informationNested Uncertain Differential Equations and Its Application to Multi-factor Term Structure Model
Nested Uncertain Differential Equations and Its Application to Multi-factor Term Structure Model Xiaowei Chen International Business School, Nankai University, Tianjin 371, China School of Finance, Nankai
More informationUNCERTAINTY FUNCTIONAL DIFFERENTIAL EQUATIONS FOR FINANCE
Surveys in Mathematics and its Applications ISSN 1842-6298 (electronic), 1843-7265 (print) Volume 5 (2010), 275 284 UNCERTAINTY FUNCTIONAL DIFFERENTIAL EQUATIONS FOR FINANCE Iuliana Carmen Bărbăcioru Abstract.
More informationSTAT 7032 Probability. Wlodek Bryc
STAT 7032 Probability Wlodek Bryc Revised for Spring 2019 Printed: January 14, 2019 File: Grad-Prob-2019.TEX Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221 E-mail address:
More informationM.PHIL. MATHEMATICS PROGRAMME New Syllabus (with effect from Academic Year) Scheme of the Programme. of Credits
I Semester II Semester M.PHIL. MATHEMATICS PROGRAMME New Syllabus (with effect from 2018 2021 Academic Year) Scheme of the Programme Subject Subject Number Exam Internal External Total Code of Duration
More informationRegular Variation and Extreme Events for Stochastic Processes
1 Regular Variation and Extreme Events for Stochastic Processes FILIP LINDSKOG Royal Institute of Technology, Stockholm 2005 based on joint work with Henrik Hult www.math.kth.se/ lindskog 2 Extremes for
More informationTime Series 2. Robert Almgren. Sept. 21, 2009
Time Series 2 Robert Almgren Sept. 21, 2009 This week we will talk about linear time series models: AR, MA, ARMA, ARIMA, etc. First we will talk about theory and after we will talk about fitting the models
More informationSTAT 7032 Probability Spring Wlodek Bryc
STAT 7032 Probability Spring 2018 Wlodek Bryc Created: Friday, Jan 2, 2014 Revised for Spring 2018 Printed: January 9, 2018 File: Grad-Prob-2018.TEX Department of Mathematical Sciences, University of Cincinnati,
More information1 Linear Regression and Correlation
Math 10B with Professor Stankova Worksheet, Discussion #27; Tuesday, 5/1/2018 GSI name: Roy Zhao 1 Linear Regression and Correlation 1.1 Concepts 1. Often when given data points, we want to find the line
More informationApproximate Bayesian Computation and Particle Filters
Approximate Bayesian Computation and Particle Filters Dennis Prangle Reading University 5th February 2014 Introduction Talk is mostly a literature review A few comments on my own ongoing research See Jasra
More informationELEG 3143 Probability & Stochastic Process Ch. 6 Stochastic Process
Department of Electrical Engineering University of Arkansas ELEG 3143 Probability & Stochastic Process Ch. 6 Stochastic Process Dr. Jingxian Wu wuj@uark.edu OUTLINE 2 Definition of stochastic process (random
More informationLecture 4: Introduction to stochastic processes and stochastic calculus
Lecture 4: Introduction to stochastic processes and stochastic calculus Cédric Archambeau Centre for Computational Statistics and Machine Learning Department of Computer Science University College London
More informationContinuum Limit of Forward Kolmogorov Equation Friday, March 06, :04 PM
Continuum Limit of Forward Kolmogorov Equation Friday, March 06, 2015 2:04 PM Please note that one of the equations (for ordinary Brownian motion) in Problem 1 was corrected on Wednesday night. And actually
More information2. Transience and Recurrence
Virtual Laboratories > 15. Markov Chains > 1 2 3 4 5 6 7 8 9 10 11 12 2. Transience and Recurrence The study of Markov chains, particularly the limiting behavior, depends critically on the random times
More informationProbability, Random Processes and Inference
INSTITUTO POLITÉCNICO NACIONAL CENTRO DE INVESTIGACION EN COMPUTACION Laboratorio de Ciberseguridad Probability, Random Processes and Inference Dr. Ponciano Jorge Escamilla Ambrosio pescamilla@cic.ipn.mx
More informationRiemannian geometry of surfaces
Riemannian geometry of surfaces In this note, we will learn how to make sense of the concepts of differential geometry on a surface M, which is not necessarily situated in R 3. This intrinsic approach
More information6. Brownian Motion. Q(A) = P [ ω : x(, ω) A )
6. Brownian Motion. stochastic process can be thought of in one of many equivalent ways. We can begin with an underlying probability space (Ω, Σ, P) and a real valued stochastic process can be defined
More informationAn Introduction to Probability Theory and Its Applications
An Introduction to Probability Theory and Its Applications WILLIAM FELLER (1906-1970) Eugene Higgins Professor of Mathematics Princeton University VOLUME II SECOND EDITION JOHN WILEY & SONS Contents I
More informationMath 307 Learning Goals
Math 307 Learning Goals May 14, 2018 Chapter 1 Linear Equations 1.1 Solving Linear Equations Write a system of linear equations using matrix notation. Use Gaussian elimination to bring a system of linear
More informationOn pathwise stochastic integration
On pathwise stochastic integration Rafa l Marcin Lochowski Afican Institute for Mathematical Sciences, Warsaw School of Economics UWC seminar Rafa l Marcin Lochowski (AIMS, WSE) On pathwise stochastic
More informationStochastic Processes. Theory for Applications. Robert G. Gallager CAMBRIDGE UNIVERSITY PRESS
Stochastic Processes Theory for Applications Robert G. Gallager CAMBRIDGE UNIVERSITY PRESS Contents Preface page xv Swgg&sfzoMj ybr zmjfr%cforj owf fmdy xix Acknowledgements xxi 1 Introduction and review
More informationINNER PRODUCT SPACE. Definition 1
INNER PRODUCT SPACE Definition 1 Suppose u, v and w are all vectors in vector space V and c is any scalar. An inner product space on the vectors space V is a function that associates with each pair of
More informationTheorem 2.1 (Caratheodory). A (countably additive) probability measure on a field has an extension. n=1
Chapter 2 Probability measures 1. Existence Theorem 2.1 (Caratheodory). A (countably additive) probability measure on a field has an extension to the generated σ-field Proof of Theorem 2.1. Let F 0 be
More informationNormal approximation of Poisson functionals in Kolmogorov distance
Normal approximation of Poisson functionals in Kolmogorov distance Matthias Schulte Abstract Peccati, Solè, Taqqu, and Utzet recently combined Stein s method and Malliavin calculus to obtain a bound for
More informationTable of Contents [ntc]
Table of Contents [ntc] 1. Introduction: Contents and Maps Table of contents [ntc] Equilibrium thermodynamics overview [nln6] Thermal equilibrium and nonequilibrium [nln1] Levels of description in statistical
More informationRecap. Probability, stochastic processes, Markov chains. ELEC-C7210 Modeling and analysis of communication networks
Recap Probability, stochastic processes, Markov chains ELEC-C7210 Modeling and analysis of communication networks 1 Recap: Probability theory important distributions Discrete distributions Geometric distribution
More informationstochnotes Page 1
stochnotes110308 Page 1 Kolmogorov forward and backward equations and Poisson process Monday, November 03, 2008 11:58 AM How can we apply the Kolmogorov equations to calculate various statistics of interest?
More informationMarkov Chains. X(t) is a Markov Process if, for arbitrary times t 1 < t 2 <... < t k < t k+1. If X(t) is discrete-valued. If X(t) is continuous-valued
Markov Chains X(t) is a Markov Process if, for arbitrary times t 1 < t 2
More informationLecture 20 : Markov Chains
CSCI 3560 Probability and Computing Instructor: Bogdan Chlebus Lecture 0 : Markov Chains We consider stochastic processes. A process represents a system that evolves through incremental changes called
More informationBrownian Motion and Poisson Process
and Poisson Process She: What is white noise? He: It is the best model of a totally unpredictable process. She: Are you implying, I am white noise? He: No, it does not exist. Dialogue of an unknown couple.
More informationStochastic Processes
Stochastic Processes A very simple introduction Péter Medvegyev 2009, January Medvegyev (CEU) Stochastic Processes 2009, January 1 / 54 Summary from measure theory De nition (X, A) is a measurable space
More informationEuclidean Space. This is a brief review of some basic concepts that I hope will already be familiar to you.
Euclidean Space This is a brief review of some basic concepts that I hope will already be familiar to you. There are three sets of numbers that will be especially important to us: The set of all real numbers,
More informationAN INTRODUCTION TO STOCHASTIC EPIDEMIC MODELS-PART I
AN INTRODUCTION TO STOCHASTIC EPIDEMIC MODELS-PART I Linda J. S. Allen Department of Mathematics and Statistics Texas Tech University Lubbock, Texas U.S.A. 2008 Summer School on Mathematical Modeling of
More informationContents. 1 State-Space Linear Systems 5. 2 Linearization Causality, Time Invariance, and Linearity 31
Contents Preamble xiii Linear Systems I Basic Concepts 1 I System Representation 3 1 State-Space Linear Systems 5 1.1 State-Space Linear Systems 5 1.2 Block Diagrams 7 1.3 Exercises 11 2 Linearization
More informationM4A42 APPLIED STOCHASTIC PROCESSES
M4A42 APPLIED STOCHASTIC PROCESSES G.A. Pavliotis Department of Mathematics Imperial College London, UK LECTURE 1 12/10/2009 Lectures: Mondays 09:00-11:00, Huxley 139, Tuesdays 09:00-10:00, Huxley 144.
More information(, ) : R n R n R. 1. It is bilinear, meaning it s linear in each argument: that is
17 Inner products Up until now, we have only examined the properties of vectors and matrices in R n. But normally, when we think of R n, we re really thinking of n-dimensional Euclidean space - that is,
More informationLecture 1: Brief Review on Stochastic Processes
Lecture 1: Brief Review on Stochastic Processes A stochastic process is a collection of random variables {X t (s) : t T, s S}, where T is some index set and S is the common sample space of the random variables.
More informationApplied Probability and Stochastic Processes
Applied Probability and Stochastic Processes In Engineering and Physical Sciences MICHEL K. OCHI University of Florida A Wiley-Interscience Publication JOHN WILEY & SONS New York - Chichester Brisbane
More informationA Concise Course on Stochastic Partial Differential Equations
A Concise Course on Stochastic Partial Differential Equations Michael Röckner Reference: C. Prevot, M. Röckner: Springer LN in Math. 1905, Berlin (2007) And see the references therein for the original
More informationITO OLOGY. Thomas Wieting Reed College, Random Processes 2 The Ito Integral 3 Ito Processes 4 Stocastic Differential Equations
ITO OLOGY Thomas Wieting Reed College, 2000 1 Random Processes 2 The Ito Integral 3 Ito Processes 4 Stocastic Differential Equations 1 Random Processes 1 Let (, F,P) be a probability space. By definition,
More informationSTA 624 Practice Exam 2 Applied Stochastic Processes Spring, 2008
Name STA 624 Practice Exam 2 Applied Stochastic Processes Spring, 2008 There are five questions on this test. DO use calculators if you need them. And then a miracle occurs is not a valid answer. There
More informationMATH4210 Financial Mathematics ( ) Tutorial 7
MATH40 Financial Mathematics (05-06) Tutorial 7 Review of some basic Probability: The triple (Ω, F, P) is called a probability space, where Ω denotes the sample space and F is the set of event (σ algebra
More informationThe Transition Probability Function P ij (t)
The Transition Probability Function P ij (t) Consider a continuous time Markov chain {X(t), t 0}. We are interested in the probability that in t time units the process will be in state j, given that it
More informationMarkov Chains. Chapter 16. Markov Chains - 1
Markov Chains Chapter 16 Markov Chains - 1 Why Study Markov Chains? Decision Analysis focuses on decision making in the face of uncertainty about one future event. However, many decisions need to consider
More informationMathematics (MA) Mathematics (MA) 1. MA INTRO TO REAL ANALYSIS Semester Hours: 3
Mathematics (MA) 1 Mathematics (MA) MA 502 - INTRO TO REAL ANALYSIS Individualized special projects in mathematics and its applications for inquisitive and wellprepared senior level undergraduate students.
More informationSimulation methods for stochastic models in chemistry
Simulation methods for stochastic models in chemistry David F. Anderson anderson@math.wisc.edu Department of Mathematics University of Wisconsin - Madison SIAM: Barcelona June 4th, 21 Overview 1. Notation
More informationContinuous Time Markov Chains
Continuous Time Markov Chains Stochastic Processes - Lecture Notes Fatih Cavdur to accompany Introduction to Probability Models by Sheldon M. Ross Fall 2015 Outline Introduction Continuous-Time Markov
More informationLTCC. Exercises. (1) Two possible weather conditions on any day: {rainy, sunny} (2) Tomorrow s weather depends only on today s weather
1. Markov chain LTCC. Exercises Let X 0, X 1, X 2,... be a Markov chain with state space {1, 2, 3, 4} and transition matrix 1/2 1/2 0 0 P = 0 1/2 1/3 1/6. 0 0 0 1 (a) What happens if the chain starts in
More informationStochastic modelling of epidemic spread
Stochastic modelling of epidemic spread Julien Arino Centre for Research on Inner City Health St Michael s Hospital Toronto On leave from Department of Mathematics University of Manitoba Julien Arino@umanitoba.ca
More informationNormed Vector Spaces and Double Duals
Normed Vector Spaces and Double Duals Mathematics 481/525 In this note we look at a number of infinite-dimensional R-vector spaces that arise in analysis, and we consider their dual and double dual spaces
More informationLecture 4 An Introduction to Stochastic Processes
Lecture 4 An Introduction to Stochastic Processes Prof. Massimo Guidolin Prep Course in Quantitative Methods for Finance August-September 2017 Plan of the lecture Motivation and definitions Filtrations
More informationAn Uncertain Control Model with Application to. Production-Inventory System
An Uncertain Control Model with Application to Production-Inventory System Kai Yao 1, Zhongfeng Qin 2 1 Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China 2 School of Economics
More informationA NOTE ON STOCHASTIC INTEGRALS AS L 2 -CURVES
A NOTE ON STOCHASTIC INTEGRALS AS L 2 -CURVES STEFAN TAPPE Abstract. In a work of van Gaans (25a) stochastic integrals are regarded as L 2 -curves. In Filipović and Tappe (28) we have shown the connection
More information6.842 Randomness and Computation March 3, Lecture 8
6.84 Randomness and Computation March 3, 04 Lecture 8 Lecturer: Ronitt Rubinfeld Scribe: Daniel Grier Useful Linear Algebra Let v = (v, v,..., v n ) be a non-zero n-dimensional row vector and P an n n
More informationMARKOV PROCESSES. Valerio Di Valerio
MARKOV PROCESSES Valerio Di Valerio Stochastic Process Definition: a stochastic process is a collection of random variables {X(t)} indexed by time t T Each X(t) X is a random variable that satisfy some
More informationChapter II. Metric Spaces and the Topology of C
II.1. Definitions and Examples of Metric Spaces 1 Chapter II. Metric Spaces and the Topology of C Note. In this chapter we study, in a general setting, a space (really, just a set) in which we can measure
More informationT 1. The value function v(x) is the expected net gain when using the optimal stopping time starting at state x:
108 OPTIMAL STOPPING TIME 4.4. Cost functions. The cost function g(x) gives the price you must pay to continue from state x. If T is your stopping time then X T is your stopping state and f(x T ) is your
More informationMathematical Analysis Outline. William G. Faris
Mathematical Analysis Outline William G. Faris January 8, 2007 2 Chapter 1 Metric spaces and continuous maps 1.1 Metric spaces A metric space is a set X together with a real distance function (x, x ) d(x,
More informationIntroduction to Computational Stochastic Differential Equations
Introduction to Computational Stochastic Differential Equations Gabriel J. Lord Catherine E. Powell Tony Shardlow Preface Techniques for solving many of the differential equations traditionally used by
More informationWeek 9 Generators, duality, change of measure
Week 9 Generators, duality, change of measure Jonathan Goodman November 18, 013 1 Generators This section describes a common abstract way to describe many of the differential equations related to Markov
More informationLecture 12. F o s, (1.1) F t := s>t
Lecture 12 1 Brownian motion: the Markov property Let C := C(0, ), R) be the space of continuous functions mapping from 0, ) to R, in which a Brownian motion (B t ) t 0 almost surely takes its value. Let
More informationThe existence of Burnett coefficients in the periodic Lorentz gas
The existence of Burnett coefficients in the periodic Lorentz gas N. I. Chernov and C. P. Dettmann September 14, 2006 Abstract The linear super-burnett coefficient gives corrections to the diffusion equation
More informationConvergence of Feller Processes
Chapter 15 Convergence of Feller Processes This chapter looks at the convergence of sequences of Feller processes to a iting process. Section 15.1 lays some ground work concerning weak convergence of processes
More informationExponential martingales: uniform integrability results and applications to point processes
Exponential martingales: uniform integrability results and applications to point processes Alexander Sokol Department of Mathematical Sciences, University of Copenhagen 26 September, 2012 1 / 39 Agenda
More information1: PROBABILITY REVIEW
1: PROBABILITY REVIEW Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) Slides 1: Probability Review 1 / 56 Outline We will review the following
More informationCDA6530: Performance Models of Computers and Networks. Chapter 3: Review of Practical Stochastic Processes
CDA6530: Performance Models of Computers and Networks Chapter 3: Review of Practical Stochastic Processes Definition Stochastic process X = {X(t), t2 T} is a collection of random variables (rvs); one rv
More informationLinear Models for Regression
Linear Models for Regression Machine Learning Torsten Möller Möller/Mori 1 Reading Chapter 3 of Pattern Recognition and Machine Learning by Bishop Chapter 3+5+6+7 of The Elements of Statistical Learning
More informationEmbeddings of finite metric spaces in Euclidean space: a probabilistic view
Embeddings of finite metric spaces in Euclidean space: a probabilistic view Yuval Peres May 11, 2006 Talk based on work joint with: Assaf Naor, Oded Schramm and Scott Sheffield Definition: An invertible
More informationModern Discrete Probability Spectral Techniques
Modern Discrete Probability VI - Spectral Techniques Background Sébastien Roch UW Madison Mathematics December 22, 2014 1 Review 2 3 4 Mixing time I Theorem (Convergence to stationarity) Consider a finite
More information