A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent...

through a mathematical model from other observable variables that can be directly observed or measured. Such latent variable models are used in many disciplines...

the variables are independent. It is called a latent class model because the class to which each data point belongs is unobserved, or latent. Latent class...

to collections of Likert scale items is to summarize them via a latent variable model, for example using factor analysis or item response theory. Likert...

another. Structural equation models often contain postulated causal connections among some latent variables (variables thought to exist but which can't...

discriminative probabilistic latent variable models (DPLVM) are a type of CRFs for sequence tagging tasks. They are latent variable models that are trained discriminatively...

latent variable models, together with a measurement model; or as probabilistic models, directly modeling the probability. The latent variable interpretation...

formulate multinomial logistic regression as a latent variable model, following the two-way latent variable model described for binary logistic regression....

possible to motivate the probit model as a latent variable model. Suppose there exists an auxiliary random variable Y ∗ = X T β + ε , {\displaystyle...

language processing, latent Dirichlet allocation (LDA) is a Bayesian network (and, therefore, a generative statistical model) for modeling automatically extracted...

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. In...

proportional hazards model. The probit version of the above model can be justified by assuming the existence of a real-valued latent variable (unobserved quantity)...

normal, all Zipfian, etc.) but with different parameters N random latent variables specifying the identity of the mixture component of each observation...

such joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors...

low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in latent semantic analysis, from which PLSA...

similar to that broadly used in latent variable models except that here the quantities playing the role of latent variables usually have an underlying dependence...

values of h. Multinomial probit is often written in terms of a latent variable model: Y i 1 ∗ = β 1 ⋅ X i + ε 1 Y i 2 ∗ = β 2 ⋅ X i + ε 2 … … Y i m ∗...

(GTM) use a point representation in the embedded space to form a latent variable model based on a non-linear mapping from the embedded space to the high-dimensional...

parameters of latent variable models. Latent variable models are statistical models where in addition to the observed variables, a set of latent variables also...

independence is the underlying assumption of latent variable models (such as factor analysis and item response theory models). The observed items are conditionally...

case is outlined below. Source: Consider a model consisting of i.i.d. latent real-valued random variables Z 1 , … , Z n {\displaystyle Z_{1},\ldots ,Z_{n}}...

Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between...

matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional...

latent tree analysis (HLTA) is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables...

Vegas Monorail, a rail transport system in Las Vegas Latent variable model, a statistical model Left ventricular mass, a cardiac measurement Legio V Macedonica...

A Thurstonian model is a stochastic transitivity model with latent variables for describing the mapping of some continuous scale onto discrete, possibly...

Other early work on EBMs proposed models that represented energy as a composition of latent and observable variables. EBMs demonstrate useful properties:...

the latent variable and other items and the latent variable. Double Monotonicity models are used most often. Monotone homogeneity models are based on...

used to estimate growth trajectories. Latent Growth Models represent repeated measures of dependent variables as a function of time and other measures...

The Latent Diffusion Model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced...