Probabilistic Independence Networks for Hidden Markov Probability Models
From MaRDI portal
Publication:3125232
DOI10.1162/neco.1997.9.2.227zbMath0869.68083OpenAlexW2147496287WikidataQ41444163 ScholiaQ41444163MaRDI QIDQ3125232
Michael I. Jordan, David Heckerman, Padhraic Smyth
Publication date: 18 March 1997
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://authors.library.caltech.edu/13671/
Related Items (10)
Network inference from temporally dependent grouped observations ⋮ Analysing grouping of nucleotides in DNA sequences using lumped processes constructed from Markov chains ⋮ Efficient sensitivity analysis in hidden Markov models ⋮ A simplified matrix formulation for sensitivity analysis of hidden Markov models ⋮ Induction of Word and Phrase Alignments for Automatic Document Summarization ⋮ Variational approximations for categorical causal modeling with latent variables ⋮ Distributed systems estimators and packet delivery prediction based on recursive Bayesian estimation ⋮ New Advances in Logic-Based Probabilistic Modeling by PRISM ⋮ Designing neural networks that process mean values of random variables ⋮ Latent class models for diary method data: Parameter estimation by local computations
Cites Work
- On the effective implementation of the iterative proportional fitting procedure
- Graphical models for associations between variables, some of which are qualitative and some quantitative
- Estimating the dimension of a model
- Sequential updating of conditional probabilities on directed graphical structures
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- An Introduction to Spatial Point Processes and Markov Random Fields
- Probabilistic expert systems and graphical modelling: a case study in drug safety
- Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
- Independence properties of directed markov fields
- Statistical Inference for Probabilistic Functions of Finite State Markov Chains
This page was built for publication: Probabilistic Independence Networks for Hidden Markov Probability Models
