## 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{ x

_{t+1}is in A | x_{t}, x_{t-1},... } = P{ x_{t+1}is in A | x_{t}}## Contents

## Use

### Example

x_{t+1} = a + bx_{t} + e_{t} is a Markov process.