A Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability of each event depends only on...

Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when...

Markov renewal processes are a class of random processes in probability and statistics that generalize the class of Markov jump processes. Other classes...

Gauss–Markov stochastic processes (named after Carl Friedrich Gauss and Andrey Markov) are stochastic processes that satisfy the requirements for both...

A continuous-time Markov chain (CTMC) is a continuous stochastic process in which, for each state, the process will change state according to an exponential...

A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as X {\displaystyle...

In probability, a discrete-time Markov chain (DTMC) is a sequence of random variables, known as a stochastic process, in which the value of the next variable...

observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it...

probability theory and statistics, the term Markov property refers to the memoryless property of a stochastic process, which means that its future evolution...

characterize continuous-time Markov processes. In particular, they describe how the probability of a continuous-time Markov process in a certain state changes...

Markov processes, Lévy processes, Gaussian processes, random fields, renewal processes, and branching processes. The study of stochastic processes uses...

theory, a Markov kernel (also known as a stochastic kernel or probability kernel) is a map that in the general theory of Markov processes plays the role...

statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion process is stochastic in...

Andrey Markov Chebyshev–Markov–Stieltjes inequalities Gauss–Markov theorem Gauss–Markov process Hidden Markov model Markov blanket Markov chain Markov decision...

In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only...

In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution...

balance in kinetics seem to be clear. A Markov process is called a reversible Markov process or reversible Markov chain if there exists a positive stationary...

In probability theory, a piecewise-deterministic Markov process (PDMP) is a process whose behaviour is governed by random jumps at points in time, but...

probability, a Markov additive process (MAP) is a bivariate Markov process where the future states depends only on one of the variables. The process { ( X (...

Markov chain Markov chain central limit theorem Continuous-time Markov process Markov process Semi-Markov process Gauss–Markov processes: processes that...

The phrase Gauss–Markov is used in two different ways: Gauss–Markov processes in probability theory The Gauss–Markov theorem in mathematical statistics...

Processing (programming language), an open-source language and integrated development environment In probability theory: Branching process, a Markov process...

colloquially, the process, in a strong sense, forgets its history. Suppose ( X t ) {\displaystyle (X_{t})} were a stationary Markov process with stationary...

contains examples of Markov chains and Markov processes in action. All examples are in the countable state space. For an overview of Markov chains in general...

More complicated processes with the Markov property, such as Markov arrival processes, have been defined where the Poisson process is a special case...

Gauss–Markov theorem Gauss–Markov process Markov blanket Markov boundary Markov chain Markov chain central limit theorem Additive Markov chain Markov additive...

The Ornstein–Uhlenbeck process is a stationary Gauss–Markov process, which means that it is a Gaussian process, a Markov process, and is temporally homogeneous...

stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability...

equation Examples of Markov chains Category of Markov kernels Perrone (2024), pp. 10–11 Pavliotis, Grigorios A. (2014). "Markov Processes and the Chapman–Kolmogorov...

subjects named for Andrey Markov: A Markov chain or Markov process, a stochastic model describing a sequence of possible events The Markov property, the memoryless...