💡 Words with a Similar Meaning to "Bayesian linear regression"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| regression analysis | In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory variables or features). |
| linear predictor function | In statistics and in machine learning, a linear predictor function is a linear function (linear combination) of a set of coefficients and explanatory variables (independent variables), whose value is used to predict the outcome of a dependent variable. |
| polynomial regression | In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). |
| linear regressionnoun | (statistics) A linear equation for deriving a single predicted value from one or more known explanatory values. |
| 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 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. |
| logistic regression | In statistics, the logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables. |
| nonlinear regression | In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. |
| log-linear model | A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model, which makes it possible to apply (possibly multivariate) linear regression. |
| posterior predictive distribution | In Bayesian statistics, the posterior predictive distribution is the distribution of possible unobserved values conditional on the observed values. |
| 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. |
| conditional probability table | In statistics, the conditional probability table is defined for a set of discrete and mutually dependent random variables to display conditional probabilities of a single variable with respect to the others (i.e., the probability of each possible value of one variable if we know the values taken on by the other variables). |
| 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. |
| generalized additive model | In statistics, a generalized additive model is a generalized linear model in which the linear response variable depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions. |
| mean and predicted response | — |
| ordinal regression | In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. |
| binomial regression | In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is the number of successes in a series of independent Bernoulli trials, where each trial has probability of success . |
| bayesian networknoun | (statistics) A directed acyclic graph whose vertices represent random variables and whose directed edges represent conditional dependencies. Each random variable can fall into any of at least two mutually disjoint states, and has a probability function which takes as inputs the states of its parent nodes and returns as output the probability of being in a certain state for a given combination of the states of its parent nodes. A node without parent nodes just has an unconditioned probability of being in some given state. |
| 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. |
| bayes' theoremnoun | (probability theory) A theorem expressed as an equation that describes the conditional probability of an event or state given prior knowledge of another event. |
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