A First Course on Stochastic Processes

@inproceedings{Karlin1968AFC,
  title={A First Course on Stochastic Processes},
  author={Samuel Karlin and Howard M. Taylor},
  year={1968}
}
Preface. Elements of Stochastic Processes. Markov Chains. The Basic Limit Theorem of Markov Chains and Applications. Classical Examples of Continuous Time Markov Chains. Renewal Processes. Martingales. Brownian Motion. Branching Processes. Stationary Processes. Review of Matrix Analysis. Index. 

Essentials of Stochastic Processes

1. Markov Chains 2. Martingales 3. Poisson Processes 4. Markov Chains 5. Renewal Theory 6. Brownian Motion

Stationary Distributions for Discrete Time Markov Chains

We continue the study of Markov chains initiated in Chap. 5. A stationary distribution is a stochastic equilibrium for the chain. We find conditions under which such a distribution exists. We are

Lecture 4 a : Continuous-Time Markov Chain Models

  • Mathematics
  • 2019
Continuous-time Markov chains are stochastic processes whose time is continuous, t ∈ [0,∞), but the random variables are discrete. Prominent examples of continuous-time Markov processes are Poisson

Introduction to Stochastic Processes

This paper presents the introductory knowledge of stochastic processes for finance majors in mind. Main topics are sample paths properties of stochastic processes such as continuous paths or

Discrete-time Markov chains

The first mathematical results for discrete-time Markov chains with a finite state space were generated by Andrey Markov [71] whose motivation was to extend the law of large numbers to sequences of

Identities for stopped markov chains and their applications

In this paper we obtain identities for some stopped Markov chains. These identities give a unified approach to many problems in optimal stopping of a Markovian sequence, extinction probability of a
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