Markov Model

Introduction

A Markov model is a stochastic process where all the values are drawn from a discrete set. In a first-order Markov process only the most recent draw affects the distribution of the next one. All such processes can be represented by a Markov transition density matrix.

P{ xt+1 is in A | xt, xt-1,... } = P{ xt+1 is in A | xt }

Contents

Use

Example

xt+1 = a + bxt + et is a Markov process.