Previous Page
|
Next Page

The HPFMM Procedure

Overview
Basic Features PROC HPFMM Contrasted with PROC FMM Assumptions Notation for the Finite Mixture Model
Getting Started
Mixture Modeling for Binomial Overdispersion: "Student," Pearson, Beer, and Yeast Modeling Zero-Inflation: Is it Better to Fish Poorly or Not to Have Fished At All?Looking for Multiple Modes: Are Galaxies Clustered?
Syntax
PROC HPFMM Statement BAYES Statement BY Statement CLASS Statement FREQ Statement ID Statement MODEL Statement OUTPUT Statement PERFORMANCE Statement PROBMODEL Statement RESTRICT Statement WEIGHT Statement
Details
A Gentle Introduction to Finite Mixture Models Log-Likelihood Functions for Response Distributions Bayesian Analysis Parameterization of Model Effects Computational Method Choosing an Optimization Algorithm Output Data Set Default Output ODS Table Names ODS Graphics
Examples
Modeling Mixing Probabilities: All Mice Are Created Equal, but Some Are More Equal The Usefulness of Custom Starting Values: When Do Cows Eat?Enforcing Homogeneity Constraints: Count and Dispersion—It Is All Over!
References

Bayesian Analysis

Subsections:

Conjugate Sampling
Metropolis-Hastings Algorithm
Latent Variables via Data Augmentation
Prior Distributions

Previous Page
|
Next Page
|
Top of Page