Everything about Gibbs sampling
Monte Carlo algorithm
In statistics, Gibbs sampling or a Gibbs sampler, also known in statistical mechanics as the heat bath algorithm, is a Markov chain Monte Carlo (MCMC) algorithm for sampling from a specified multivariate probability distribution when direct sampling from the joint distribution is difficult, but sampling from the conditional distribution is more practical. This sequence can be used to approximate the joint distribution ; to approximate the marginal distribution of one of the variables, or some subset of the variables ; or to compute an integral. Typically, some of the variables correspond to observations whose values are known, and hence do not need to be sampled. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Gibbs samplingA 5-minute orientation
- Skim a recent paper: Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli FilteringInfluential work since 2022
- Pick up a classic book: Contemporary musiciansThe most-reprinted book on the subject
- Read the landmark paper: Variable Selection via Gibbs SamplingCited 1,999 times
- Go sideways: Bayesian statisticsA closely related topic
- Go sideways: Laplace's approximationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Variable Selection via Gibbs Sampling
On Gibbs sampling for state space models
Gibbs Sampling Methods for Stick-Breaking Priors
Adaptive Rejection Sampling for Gibbs Sampling
Adaptive Rejection Metropolis Sampling within Gibbs Sampling
Gibbs sampling methods for Bayesian quantile regression
Gibbs Sampling
Modelling Complexity: Applications of Gibbs Sampling in Medicine
🆕 Influential since 2022
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering
High-Quality Thermal Gibbs Sampling with Quantum Annealing Hardware
Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling
AlBERTino for stock price prediction: a Gibbs sampling approach
ALiSa: Acrostic Linguistic Steganography Based on BERT and Gibbs Sampling
📚 Books worth reading
Contemporary musiciansTracie Ratiner · 2010
Bayesian Methods for Management and BusinessEugene D. Hahn · 2014- 📕State-Space Models with Regime SwitchingChang-Jin Kim, Nelson, Charles R. · 1999
Data science from scratchJoel Grus · 2015
Biobehavioral aspects of aggressionDavid A. Hamburg · 1980- 📕Bayesian Statistics for the Social SciencesDavid Kaplan · 2014
Introduction to probability simulation and Gibbs sampling with REric A. Suess · 2006
Chaos, Creativity, and CultureKenan Heise · 1998
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Bayesian statistics Bayesian statistics (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a theory in the field of statistics based on the Bayesian interpretation of probability…
- Laplace's approximation Laplace's approximation or the quadratic approximation (QUAP) provides an analytical expression for a posterior probability distribution by fitting a Gaussian…
- Bayesian hierarchical modeling Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the posterior distribution of model…
- Bayesian econometrics Bayesian econometrics is a branch of econometrics which applies Bayesian principles to economic modelling. Bayesianism is based on a degree-of-belief interpreta…
- Metropolis–Hastings algorithm In statistics and statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random…
- Exponential family random graph models Exponential family random graph models (ERGMs) are a set of statistical models used to study the structure and patterns within networks, such as those…
- Point estimation In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate, since it identifies a point rather…
- Nonlinear mixed-effects model Nonlinear mixed-effects models constitute a class of statistical models generalizing linear mixed-effects models. Like linear mixed-effects models, they…