An introduction to hidden Markov models and Bayesian networks

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 15, Issue 1, 2001, Pages 9-42

  Ghahramani, Z ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Dynamic Bayesian networks; EM algorithms; Gibbs sampling; Hidden Markov models; State space models; Variational methods

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; STATE SPACE METHODS; VARIATIONAL TECHNIQUES;

DYNAMIC BAYESIAN NETWORKS; EXPECTATION MAXIMIZATION (EM) ALGORITHMS; GIBBS SAMPLING; HIDDEN MARKOV MODELS (HMM);

NEURAL NETWORKS;

EID: 0035247059     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001401000836     Document Type: Article

References (59)

1. Inferring parameters and structure of latent variable models by variational Bayes
2. Hidden Markov models of biological primary sequence information
3. A maximization technique occurring in the statistical analysis of probabilistic functions of Markovchains
4. An input-output HMM architecture
5. Spatial interaction and the statistical analysis of lattice systems
6. Variational PCA
7. Mixtures of controllers for jump linear and nonlinear plants
8. A model for reasoning about persistence and causation
9. Maximum likelihood from incomplete data via the EM algorithm
10. ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition
11. Transformed component analysis: Joint estimation of spatial transformations and image components
12. Multivariate adaptive regression splines
13. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
14. Factorial learning and the EM algorithm
15. Variational inference for Bayesian mixtures of factor analysers
16. Variational learning for switching state space models
17. Factorial hidden Markov models
18. Autoencoders, minimum description length, and Helmholtz free energy
19. Keeping neural networks simple by minimizing the description length of the weights
20. Adaptive mixture of local experts
21. Bayesian updating in recursive graphical models by local computations
22. Hidden Markov decision trees
23. Hierarchical mixtures of experts and the EM algorithm
24. Hidden Markov models for speech recognition
25. New results in linear filtering and prediction
26. Stochastic simulation algorithms for dynamic probabilistic networks
27. A computational model for causal and diagnostic reasoning in inference systems
28. Hidden Markov models in computational biology: Applications to protein modeling
29. Local computations with probabilities on graphical structures and their application to expert systems
30. Learning fine motion by Markov mixtures of experts
31. An introduction to hidden Markov models
32. Solutions to the linear smoothing problem
33. Bayesian estimation of hidden Markov chains: A stochastic implementation
34. A unifying review of linear Gaussian models
35. Mixed memory Markov models
36. Modeling acoustic correlations by factor analysis
37. Exploiting tractable substructures in intractable networks
38. An approach to time series smoothing and forecasting using the EM algorithm
39. Hidden Markov models for fault detection in dynamic systems
40. Probabilistic independence networks for hidden Markov probability models
41. Hidden Markov model induction by Bayesian model merging
42. The calculation of posterior distributions by data augmentation
43. Error bounds for convolutional codes and an asymptotically optimal decoding algorithm
44. Bayesian methods for mixtures of experts