Metropolis–Hastings algorithm
The Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This algorithm is named after Nicholas Metropolis and W. K. Hastings.
Overview[edit | edit source]
The Metropolis–Hastings algorithm generates a sequence of samples from the target distribution by constructing a Markov chain that has the desired distribution as its equilibrium distribution. The algorithm proceeds by proposing a move to a new state and then deciding whether to accept or reject the move based on a certain acceptance criterion.
Algorithm[edit | edit source]
The algorithm can be described as follows: 1. Initialization: Start with an initial state \( x_0 \). 2. Iteration: For each iteration \( t \):
a. Propose a new state \( x' \) based on the current state \( x_t \) using a proposal distribution \( q(x'|x_t) \). b. Calculate the acceptance ratio: \[ \alpha = \min\left(1, \frac{\pi(x') q(x_t|x')}{\pi(x_t) q(x'|x_t)}\right) \] where \( \pi \) is the target distribution. c. Accept the new state with probability \( \alpha \). If the new state is accepted, set \( x_{t+1} = x' \); otherwise, set \( x_{t+1} = x_t \).
Properties[edit | edit source]
The Metropolis–Hastings algorithm has several important properties:
- Ergodicity: The Markov chain is ergodic, meaning that it will eventually explore the entire state space given enough time.
- Detailed balance: The algorithm satisfies the detailed balance condition, ensuring that the target distribution is the stationary distribution of the Markov chain.
- Convergence: Under certain conditions, the samples generated by the algorithm will converge to the target distribution.
Applications[edit | edit source]
The Metropolis–Hastings algorithm is widely used in various fields, including:
Variants[edit | edit source]
Several variants of the Metropolis–Hastings algorithm exist, including:
- Gibbs sampling: A special case where the proposal distribution is chosen to be the conditional distribution of each variable given the others.
- Adaptive Metropolis–Hastings: An extension that adapts the proposal distribution based on the history of the chain.
See also[edit | edit source]
References[edit | edit source]
Search WikiMD
Ad.Tired of being Overweight? Try W8MD's physician weight loss program.
Semaglutide (Ozempic / Wegovy and Tirzepatide (Mounjaro / Zepbound) available.
Advertise on WikiMD
WikiMD's Wellness Encyclopedia |
Let Food Be Thy Medicine Medicine Thy Food - Hippocrates |
Translate this page: - East Asian
中文,
日本,
한국어,
South Asian
हिन्दी,
தமிழ்,
తెలుగు,
Urdu,
ಕನ್ನಡ,
Southeast Asian
Indonesian,
Vietnamese,
Thai,
မြန်မာဘာသာ,
বাংলা
European
español,
Deutsch,
français,
Greek,
português do Brasil,
polski,
română,
русский,
Nederlands,
norsk,
svenska,
suomi,
Italian
Middle Eastern & African
عربى,
Turkish,
Persian,
Hebrew,
Afrikaans,
isiZulu,
Kiswahili,
Other
Bulgarian,
Hungarian,
Czech,
Swedish,
മലയാളം,
मराठी,
ਪੰਜਾਬੀ,
ગુજરાતી,
Portuguese,
Ukrainian
WikiMD is not a substitute for professional medical advice. See full disclaimer.
Credits:Most images are courtesy of Wikimedia commons, and templates Wikipedia, licensed under CC BY SA or similar.
Contributors: Prab R. Tumpati, MD