Algorithms that estimate hidden quantities or draw samples from probability distributions, such as expectation-maximization, the Viterbi algorithm, the Baum-Welch algorithm, Metropolis-Hastings and the junction tree algorithm. An algorithm belongs here when its cited article describes statistical inference, a hidden Markov model or Markov chain Monte Carlo sampling. It excludes decision tree and clustering methods. A member is placed here by what its own cited article states about it, not by the name of the algorithm or by the uses it is put to today, so each entry sits in the group its source describes it as belonging to.
All Probabilistic Inference and Sampling Algorithms
Filter Results11 entries
Sources
Generalized distributive law (Wikipedia)
Wikipedia: Expectation Maximization Algorithm
Wikimedia FoundationHas Member: Expectation Maximization Algorithm, Lead sectionQuote, Has Member: Expectation Maximization Algorithm, Lead section
algorithm is an iterative method to find (local) maximum likelihood
Viterbi Algorithm (Wikipedia)
Has Member: Viterbi Algorithm, Lead sectionQuote, Has Member: Viterbi Algorithm, Lead section
finds the most likely sequence of hidden events
Baum-Welch algorithm (Wikipedia)
Has Member: Baum-Welch Algorithm, Lead sectionQuote, Has Member: Baum-Welch Algorithm, Lead section
is a special case of the expectation-maximization algorithm
Metropolis-Hastings algorithm (Wikipedia)
Has Member: Metropolis-Hastings Algorithm, Lead sectionQuote, Has Member: Metropolis-Hastings Algorithm, Lead section
Markov chain Monte Carlo (MCMC) method
Wikipedia: Island algorithm
Has Member: Island Algorithm, Lead sectionQuote, Has Member: Island Algorithm, Lead section
performing inference on hidden Markov models
Junction tree algorithm (Wikipedia)
Has Member: Junction tree algorithm, Lead sectionQuote, Has Member: Junction tree algorithm, Lead section
method used in machine learning to extract marginalization in general graphs
Wikipedia: Naive Bayes Classifier Algorithm
Wikimedia FoundationHas Member: Naive Bayes Classifier Algorithm, LeadQuote, Has Member: Naive Bayes Classifier Algorithm, Lead
In statistics, naive (sometimes simple or idiot's) Bayes classifiers are a family of "probabilistic classifiers" which assume that the features are conditionally independent, given the target class.
Gibbs sampling (Wikipedia)
Has Member: Gibbs Sampling Algorithm, LeadQuote, Has Member: Gibbs Sampling Algorithm, Lead
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.
Wikipedia: Minimum message length
Has Member: Minimum Message Length, LeadQuote, Has Member: Minimum Message Length, Lead
Minimum message length (MML) is a Bayesian information-theoretic method for statistical model comparison and selection.
Kalman filter (Wikipedia)
Has Member: Kalman Filter Algorithm, LeadQuote, Has Member: Kalman Filter Algorithm, Lead
In statistics and control theory, Kalman filtering (also known as linear quadratic estimation) is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement, by estimating a joint probability distribution over the variables for each time-step.
Reader Challenges (0)
No disputes yet. Spotted an error or a better source? Open the first one.
Sign in to dispute this or suggest a correction.