💡 Words with a Similar Meaning to "Approximate bayesian computation"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| 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. |
| empirical bayes method | Empirical Bayes methods are procedures for statistical inference in which the prior probability distribution is estimated from the data. |
| nested sampling algorithm | The nested sampling algorithm is a computational approach to the Bayesian statistics problems of comparing models and generating samples from posterior distributions. |
| 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. |
| posterior predictive distribution | In Bayesian statistics, the posterior predictive distribution is the distribution of possible unobserved values conditional on the observed values. |
| 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. |
| 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. |
| probabilistic programming | a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. |
| 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. |
| 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. |
| dynamic causal modeling | a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. |
| hyperparameternoun | (machine learning) A parameter whose value is set before the learning process begins. |
| bayesian statistics | (or ) a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. |
| bayesian vector autoregression | In statistics and econometrics, Bayesian vector autoregression uses Bayesian methods to estimate a vector autoregression model. |
| markov chain monte carlo | In statistics, Markov chain Monte Carlo is a class of algorithms used to draw samples from a probability distribution. |
| bayesian epistemology | a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. |
| bayesian experimental design | Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. |
| probabilistic classification | In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. |
| posterior probabilitynoun | (statistics) The probability of an event given the knowledge of occurrence of other events that bear on it: P( A | B ) as contrasted to P( A ), where A is the sought event and B is the sign or symptom event. |
| bayesian linear regression | a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often labelled ) conditional on observed values of the regressors (usually ). |
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