JSTOR Daily

ERGODIC THEORY FOR CONTROLLED MARKOV CHAINS WITH STATIONARY INPUTS

Consider a stochastic process X on a finite state space X = {1,..., d}. It is conditionally Markov, given a real-valued “input process” ζ. This is assumed to be small, which is modeled through the ...
inc42

What Is Markov Chain Monte Carlo (MCMC)? Here’s All You Need to Know

What Is Markov Chain Monte Carlo? Markov Chain Monte Carlo (MCMC) is a powerful technique used in statistics and various scientific fields to sample from complex probability distributions. It is ...
JSTOR Daily

The Simplex and Policy-Iteration Methods Are Strongly Polynomial for the Markov Decision Problem with a Fixed Discount Rate

This is a preview. Log in through your library . Abstract We prove that the classic policy-iteration method [Howard, R. A. 1960. Dynamic Programming and Markov Processes. MIT, Cambridge] and the ...
Hackaday

Stochastic Markov Beats

[Attoparsec] has been building intriguing musical projects on his YouTube channel for a while and his latest is no exception. Dubbed simply as “Node Module”, it is a rack-mounted hardware-based Markov ...
Nature

Markov Chains and Hidden Markov Models

Markov chains provide a fundamental framework for modelling stochastic processes, where the next state depends solely on the current state. Hidden Markov models (HMMs) extend this framework by ...