On model selection and concavity for finite mixture models

IEEE International Symposium on Information Theory - Proceedings

Volumn , Issue , 2000, Pages 323-

  Cadez, I V ^a   Smyth, P ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FINITE MIXTURE MODEL; MAXIMUM LOG-LIKELIHOOD;

APPROXIMATION THEORY; MARKOV PROCESSES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PATTERN RECOGNITION; PROBABILITY DENSITY FUNCTION; THEOREM PROVING;

INFORMATION THEORY;

EID: 0034449217     PISSN: 21578095     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Estimating the dimension of a model
2. How many clusters? Which clustering method? Answers via model-based cluster analysis
3. Model selection for probabilistic clustering using cross-validated likelihood
4. Mixture density estimation