In statistics, a linear probability model (LPM) is a special case of a binary regression model. Here the dependent variable for each observation takes...

generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to...

In statistics, a 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...

The word is a portmanteau, coming from probability + unit. The purpose of the model is to estimate the probability that an observation with particular characteristics...

variable. Generally the probability of the two alternatives is modeled, instead of simply outputting a single value, as in linear regression. Binary regression...

the joint probability distribution P ( X , Y ) {\displaystyle P(X,Y)} on a given observable variable X and target variable Y; A generative model can be used...

term linear model refers to any model which assumes linearity in the system. The most common occurrence is in connection with regression models and the...

statistical model represents, often in considerably idealized form, the data-generating process. When referring specifically to probabilities, the corresponding...

In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory...

sampling Linear classifier Linear discriminant analysis Linear least squares Linear model Linear prediction Linear probability model Linear regression...

takes value 1 with probability p and value 0 with probability q = 1 − p. The Rademacher distribution, which takes value 1 with probability 1/2 and value −1...

analysis proceeds with least-squares linear regression, the model is called the linear probability model. Nonlinear models for binary dependent variables include...

statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression...

of probit, the link is the cdf of the normal distribution. The linear probability model is not a proper binomial regression specification because predictions...

general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that...

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

squares method can be categorized into linear and nonlinear forms, depending on the relationship between the model parameters and the observed data. The...

do not require such predictive probabilities. A variant of the previously described discriminative model is the linear-chain conditional random field...

which have been superseded by large language models. It is based on an assumption that the probability of the next word in a sequence depends only on...

a linear classifier w → {\displaystyle {\vec {w}}} . They can be generative and discriminative models. Methods of the former model joint probability distribution...

as linear. A model is considered to be nonlinear otherwise. The definition of linearity and nonlinearity is dependent on context, and linear models may...

than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically...

others. Unlike generative modelling, which studies the joint probability P ( x , y ) {\displaystyle P(x,y)} , discriminative modeling studies the P ( y | x...

mathematical model describing the observations available at a particular time. After the arrival of new information, the current posterior probability may serve...

The linear no-threshold model (LNT) is a dose-response model used in radiation protection to estimate stochastic health effects such as radiation-induced...

more widely used than parametric models, AFT models are predominantly fully parametric i.e. a probability distribution is specified for log ⁡ ( T 0 ) {\displaystyle...

approaches have been explored to adapt linear regression methods to a domain where the output is a probability value ( 0 , 1 ) {\displaystyle (0,1)} ...

output probability distribution only depends on the current channel input. A channel model may either be digital or analog. In a digital channel model, the...

and urbanism, and they can describe both linear and nonlinear processes. Causal models are mathematical models representing causal relationships within...

statistics and probability is a list of definitions of terms and concepts used in the mathematical sciences of statistics and probability, their sub-disciplines...