As the foregoing course description, in this course, the goal is to know different types of modeling and analysis of practical phenomena in terms of stochastic processes will be introduced. The content of this course include basic stochastic processes, stochastic models, and diffusion processes. The course covers the following topics：Markov models (including Poisson processes, discrete-time and continuous-time Markov chains), renewal processes, and Brownian motion etc.

In this semester we will cover the main chapters and R examples from the textbook: Introduction to Stochastic Processes using R.

As the foregoing course description, in this course, the goal is to know different types of modeling and analysis of practical phenomena in terms of stochastic processes will be introduced. The content of this course include basic stochastic processes, stochastic models, and diffusion processes. The course covers the following topics: Markov models (including Poisson processes, discrete-time and continuous-time Markov chains), renewal processes, and Brownian motion etc.

In this semester we will cover the main chapters and R examples from the textbook: Introduction to Stochastic Processes using R.

The objective of this course is to introduce basic concepts for stochastic processes. The main focus will be on studying analytical models for systems which change states stochastically with time. This is a course for studying probabilistic models rather than statistical models. Thus, background on probability and mathematical statistics are necessary. We will begin right after �onditional probability�and �onditional expectation� Students will learn concepts and techniques for characterizing models stochastically. This will help students for further study. The topics of this course include basic processes, stochastic models, and diffusion processes. Contents of this course might be adjusted according to time limitation and students�interests. They are:
1.Preliminaries: lack of memory property, transformations, inequalities, limit theorems, notations of stochastic processes
2.Markov chains: Chapman-Kolmogorov equation, classification of chains, long run behavior of Markov chains, branch processes, random walk
3.Poisson processes: Inter-arrival time distributions, conditional waiting time distributions, non-homogeneous Poisson processes
4.Continuous-time Markov chains: birth-death processes, compound Poisson processes, finite-state Markov chains
5.Renewal processes: renewal functions, limit theorems, delayed and stationary renewal processes, queueing
6.Stochastic models: Markov renewal processes, marked processes
7.Martingales: conditional expectations, filtrations, stopping time, martingale CLT
8.Diffusion Processes: Brownian motions, It�s formula, Black-Scholes Model, Girsanov Theorem

The objective of this course is to introduce basic concepts for stochastic processes. The main focus will be on studying analytical models for systems which change states stochastically with time. This is a course for studying probabilistic models rather than statistical models. Thus, background on probability and mathematical statistics are necessary. stochastic models, and diffusion processes. Contents of this course might be adjusted according to time limitation and students’ interests. They are:
1.Preliminaries: lack of memory property, transformations, inequalities, limit theorems, notations of stochastic processes
2.Markov chains: Chapman-Kolmogorov equation, classification of chains, long run behavior of Markov chains, branch processes, random walk
3.Poisson processes: Inter-arrival time distributions, conditional waiting time distributions, non-homogeneous Poisson processes
4.Continuous-time Markov chains: birth-death processes, compound Poisson processes, finite-state Markov chains
5.Renewal processes: renewal functions, limit theorems, delayed and stationary renewal processes, queuing
6.Stochastic models: Markov renewal processes, marked processes
7.Martinales: conditional expectations, filtrations, stopping time, martingale CLT
8.Diffusion Processes: Brownian motions, Its formula, Black-Scholes Model, Girsanov Theorem

Introduction to Stochastic Processes Using R (Springer)

Author: Sivaprasad Madhira · Shailaja Deshmukh

Introduction to Stochastic Processes Using R (Springer)

Author: Sivaprasad Madhira · Shailaja Deshmukh