Discrete-time Markov chains — Expert reference

Introduction

What this guide covers

Rigorous and exhaustive treatment of Markovian stochastic processes in discrete time and space: from foundational axioms to convergence theorems, with complete classification of states, absorption analysis and solved applications.

Overview

Topics covered

• Introduction
• Discrete Time Markov Chains
• Stochastic Processes: General Framework
• Probability Space and Fundamental Notation
• Taxonomy of Stochastic Processes
• Historical Context

Related concepts

Main connections

• Linear algebra & matrices
• Markov chain Monte Carlo (MCMC)
• Mean & expected value
• Poisson distribution
• Supervised machine learning
• Calculus & differentiation

Compatible notation

Mathematical sample in LaTeX

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