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Bayesian Analysis
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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
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