Conditional Density Estimation Network Construction and Evaluation
Bernoulli-gamma distribution
Bernoulli-lognormal distribution
Bernoulli-Weibull distribution
Conditional Density Estimation Network Construction and Evaluation (Ca...
Cost function for CDEN model fitting
Fit a CDEN model
Initialize a CDEN weight vector
Predict conditional distribution parameters from a fitted CDEN model
Convert a factor to a matrix of dummy codes
GAM-style effects plots for interpreting CDEN models
Logistic sigmoid function
Pareto 2 (Lomax) and Bernoulli-Pareto 2 distributions
Radial basis function kernel
Resilient backpropagation (Rprop) optimization algorithm
Cross-validation indices with a buffer between training/validation dat...
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.