Learning dynamic Bayesian networks
Z Ghahramani - International School on Neural Networks, Initiated by …, 1997 - Springer
Bayesian networks are a concise graphical formalism for describing probabilistic models.
We have provided a brief tutorial of methods for learning and inference in dynamic Bayesian …
We have provided a brief tutorial of methods for learning and inference in dynamic Bayesian …
Markov chain Monte Carlo methods: computation and inference
S Chib - Handbook of econometrics, 2001 - Elsevier
This chapter reviews the recent developments in Markov chain Monte Carlo simulation
methods. These methods, which are concerned with the simulation of high dimensional …
methods. These methods, which are concerned with the simulation of high dimensional …
[图书][B] Time series analysis by state space methods
J Durbin, SJ Koopman - 2012 - books.google.com
This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …
time series analysis. The distinguishing feature of state space time series models is that …
[图书][B] Gaussian Markov random fields: theory and applications
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …
very active area of research in which few up-to-date reference works are available. This is …
[图书][B] Dynamic bayesian networks: representation, inference and learning
KP Murphy - 2002 - search.proquest.com
Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …
Filtering via simulation: Auxiliary particle filters
MK Pitt, N Shephard - Journal of the American statistical …, 1999 - Taylor & Francis
This article analyses the recently suggested particle approach to filtering time series. We
suggest that the algorithm is not robust to outliers for two reasons: The design of the …
suggest that the algorithm is not robust to outliers for two reasons: The design of the …
[图书][B] Finite mixture and Markov switching models
S Frühwirth-Schnatter - 2006 - Springer
Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …
applications exploding. Finite mixture models are nowadays applied in such diverse areas …
[PDF][PDF] Bayesian filtering: From Kalman filters to particle filters, and beyond
Z Chen - Statistics, 2003 - automatica.dei.unipd.it
In this self-contained survey/review paper, we systematically investigate the roots of
Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is …
Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is …
An introduction to hidden Markov models and Bayesian networks
Z Ghahramani - International journal of pattern recognition and …, 2001 - World Scientific
We provide a tutorial on learning and inference in hidden Markov models in the context of
the recent literature on Bayesian networks. This perspective makes it possible to consider …
the recent literature on Bayesian networks. This perspective makes it possible to consider …
Particle filters for state estimation of jump Markov linear systems
Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time
according to a finite state Markov chain. In this paper, our aim is to recursively compute …
according to a finite state Markov chain. In this paper, our aim is to recursively compute …