EM Algorithm and Related Statistical Models
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advanced missing data techniques
Asymptotic Variance Covariance Matrix
Bayesian computation methods
Boltzmann Machine
categorical data inference
Category=PBT
Category=UMB
Category=UY
Conditional Expectation
DA Algorithm
data
density
Em Algorithm
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
function
GS Algorithm
Hidden Units
Imputed Data
incomplete
Latent Structure Model
latent variable analysis
maximum likelihood estimation
MCEM
MCEM Algorithm
MCMC Method
method
missing
Missing Data
Missing Values
Missingness Pattern
Mixed Linear Model
Model Manifold
Multivariate Normal Distribution Model
neural network optimization
newton
Newton Raphson Method
Nonnegative Definite
Original Em
Parameter Update Rule
Posterior Distribution
probability
quasi-Newton Method
raphson
REML Estimation
robust statistical modeling
value
Product details
- ISBN 9780824747015
- Weight: 560g
- Dimensions: 152 x 229mm
- Publication Date: 15 Oct 2003
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Exploring the application and formulation of the EM algorithm, The EM Algorithm and Related Statistical Models offers a valuable method for constructing statistical models when only incomplete information is available, and proposes specific estimation algorithms for solutions to incomplete data problems. The text covers current topics including statistical models with latent variables, as well as neural network models, and Markov Chain Monte Carlo methods. It describes software resources valuable for the processing of the EM algorithm with incomplete data and for general analysis of latent structure models of categorical data, and studies accelerated versions of the EM algorithm.
Michiko Watanabe, Kazunori Yamaguchi
