Visual Studio Magazine

Probit Regression Using C#

Probit ("probability unit") regression is a classical machine learning technique that can be used for binary classification -- predicting an outcome that can only be one of two discrete values. For ...
GitHub

Indicator-Based Bayesian Variable Selection for Ordinal Probit Regression Model

Data Generation Function: Simulates datasets suitable for Ordinal Probit Regression Model. Model Fitting + AIC-Based Variable Selection Model Fitting + BIC-Based Variable Selection Model Fitting + ...
IEEE

Spatial probit regression model: Recursive importance sampling approach

Abstract: The probit regression model is a model used to analyze the relationship between categorical response variables, with predictive variables that are numerical, categorical, or the combination ...
JSTOR Daily

Likelihood Inference in a Correlated Probit Regression Model

Equicorrelated binary observations are modelled using a multivariate probit regression model. Log likelihood derivatives are reduced to simple linear combinations of equicorrelated multivariate normal ...
Simon Fraser University

Example 45.3: Probit Model with Likelihood function

and is the normal probability function. This is the likelihood function for a binary probit model. This likelihood is strictly positive so that you can take a square root of and use this as your ...
JSTOR Daily

Predicting Germination Response to Temperature. II. Three-dimensional Regression, Statistical Gridding and Iterative-probit Optimization Using Measured and Interpolated ...

• Background and Aims Most current thermal-germination models are parameterized with subpopulation-specific rate data, interpolated from cumulative-germination-response curves. The purpose of this ...