May 1, 2024
3 minute read
Markov models are a family of stochastic processes widely used in various fields, including natural language processing, speech recognition, bioinformatics, and finance. They are named after the Russian mathematician Andrey Markov, who first introduced them in 1906. Markov models are based on the assumption that the future state of a system depends only on its present state, not on its past history.
Markov Properties
The key feature of Markov models is the Markov property, which states that the conditional probability of future states depends only on the current state, not on the sequence of events that led to that state. This property simplifies modeling complex systems by allowing us to focus only on the current state and its immediate successor.
8yl8qh|
Find a path to becoming a Markov Models. Learn more at:
OpenCourser.com/topic/8yl8qh/markov
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Markov Models.
This advanced textbook introduces the theoretical and practical aspects of Markov Chain Monte Carlo (MCMC). covers the computational tools used for MCMC simulations such as Markov chains, Metropolis-Hastings algorithms, Bayesian inference and statistical computing.
This textbook explores the theory and applications of hidden Markov models (HMMs). The book includes a discussion of both continuous-time and discrete-time models with applications to signal processing, finance, and bioinformatics.
This advanced textbook focuses on the mathematical theory of Markov chains. It covers topics such as ergodicity, convergence rates, and stability.
This advanced textbook introduces the theory and applications of Markov decision processes. The book includes applications in operations research, economics, and reinforcement learning.
This textbook presents a comprehensive treatment of discrete-time Markov chains. It includes applications in finance, insurance, and queuing theory.
This textbook provides a comprehensive introduction to Markov chains and stochastic processes. It includes applications in queueing theory, population genetics, and finance.
This textbook mainly discusses the properties and analysis of Markov chains and their use in a variety of areas. The coverage includes theoretical results, numerical methods, and modern applications.
This computational book presents numerical methods for Markov chains. It covers topics such as Markov chain Monte Carlo (MCMC) and spectral methods.
Introduces the theory and practice of Markov modeling for time series data. The book includes applications in finance, economics, and climate science.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/8yl8qh/markov
Save
