💡 Words with a Similar Meaning to "Nested sampling algorithm"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| approximate bayesian computation | Approximate Bayesian computation constitutes a class of computational methods rooted in Bayesian statistics that can be used to estimate the posterior distributions of model parameters. |
| bayesian hierarchical modeling | Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. |
| hierarchical dirichlet process | In statistics and machine learning, the hierarchical Dirichlet process is a nonparametric Bayesian approach to clustering grouped data. |
| bayesian structural time series | Bayesian structural time series model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. |
| dynamic causal modeling | a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. |
| markov chain monte carlo | In statistics, Markov chain Monte Carlo is a class of algorithms used to draw samples from a probability distribution. |
| rejection samplingnoun | (computational statistics, numerical analysis) A technique used to generate observations from a distribution by repeatedly sampling an approximation function that bounds the distribution everywhere from above and rejecting the samples based on the quotient of the two functions at the sample's location. |
| neural network gaussian process | A Neural Network Gaussian Process is a Gaussian process obtained as the limit of a certain type of sequence of neural networks. |
| recursive bayesian estimation | In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach for estimating an unknown probability density function recursively over time using incoming measurements and a mathematical process model. |
| empirical bayes method | Empirical Bayes methods are procedures for statistical inference in which the prior probability distribution is estimated from the data. |
| posterior predictive distribution | In Bayesian statistics, the posterior predictive distribution is the distribution of possible unobserved values conditional on the observed values. |
| bayesian inference in phylogeny | Bayesian inference of phylogeny combines the information in the prior and in the data likelihood to create the so-called posterior probability of trees, which is the probability that the tree is correct given the data, the prior and the likelihood model. |
| monte carlo methodnoun | (mathematics, statistics) Any of a class of techniques for estimating the solution of a numerical mathematical problem by means of a random artificial sampling experiment that simulates the problem. |
| numerical analysisnoun | (mathematics) The study of algorithms to solve mathematical problems concerning continuous sets of values (such as the real numbers, complex numbers or vector spaces). |
| probabilistic programming | a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. |
| bayesian inference | (or ) a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available. |
| particle filter | Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. |
| bayesian experimental design | Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. |
| backfitting algorithm | — |
| particle swarm optimization | In computational science, particle swarm optimization is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. |
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