See this document in CiteSeerX!

Building Blocks For Variational Bayesian Learning Of Latent Variable Models (2006)  (Make Corrections)  (1 citation)
Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen



  Home/Search   Context   Related

 
View or download:
cis.hut.fi/praiko/papers/E4.pdf
Cached:  PS.gz  PS  PDF   Image  Update  Help

From:  cis.hut.fi/praiko/ (more)
(Enter author homepages)

Rate this article: (best)
  Comment on this article  
(Enter summary)

Abstract: We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, and delay. A large variety of latent variable models can be constructed from these blocks, including variance models and nonlinear modelling, which are lacking from most existing variational systems. The introduced blocks are designed to fit together and to yield e#cient update rules. Practical implementation of... (Update)

Cited by:   More
Bayes Blocks: An Implementation of the Variational .. - Harva, Raiko.. (2005)   (Correct)

Active bibliography (related documents):   More   All
2.0:   Advances in Nonlinear Blind Source Separation - Jutten, Karhunen   (Correct)
1.4:   Variational Inference for Dirichlet Process Mixtures - David M. Blei, Michael I.. (2006)   (Correct)
1.3:   Hierarchical Nonlinear Factor Analysis - Raiko (2001)   (Correct)

Similar documents based on text:
6.0:   Unknown -   (Correct)

BibTeX entry:   (Update)

T. Raiko, H. Valpola, M. Harva, and J. Karhunen. Building blocks for variational Bayesian learning of latent variable models. 2005. Under preparation. http://citeseer.ist.psu.edu/raiko06building.html   More

@misc{ raiko05building,
  author = "T. Raiko and H. Valpola and M. Harva and J. Karhunen",
  title = "Building blocks for variational Bayesian learning of latent variable models",
  text = "T. Raiko, H. Valpola, M. Harva, and J. Karhunen. Building blocks for variational
    Bayesian learning of latent variable models. 2005. Under preparation.",
  year = "2005",
  url = "citeseer.ist.psu.edu/raiko06building.html" }
Citations (may not include all citations):
1662   Neural Networks for Pattern Recognition (context) - Bishop - 1995
1543   Probabilistic Reasoning in Intelligent Systems: Networks of .. (context) - Pearl - 1988
1213   Self-Organizing Maps (context) - Kohonen - 2001
704   Neural Networks -- A Comprehensive Foundation (context) - Haykin - 1998
475   Estimating the dimension of a model (context) - Schwarz - 1978
406   Sequential Monte Carlo Methods in Practice (context) - Doucet, de Freitas et al. - 2001
286   Optimal Filtering (context) - Anderson, Moore - 1979
269   Bayesian Learning for Neural Networks (context) - Neal - 1996
261   Bayesian Data Analysis (context) - Gelman, Carlin et al. - 1995
246   Independent Component Analysis (context) - Hyvarinen, Karhunen et al. - 2001
245   An introduction to variational methods for graphical models - Jordan, Ghahramani et al. - 1999
153   A practical Bayesian framework for backpropagation networks (context) - MacKay - 1992
120   A variational Bayesian framework for graphical models - Attias - 2000
111   Independent factor analysis - Attias - 1999
110   Learning in Graphical Models (context) - Jordan - 1999
88   Propagation algorithms for variational Bayesian learning - Ghahramani, Beal - 2001
63   Keeping neural networks simple by minimizing the description.. - Hinton, van Camp - 1993
52   Introduction to Monte Carlo methods (context) - MacKay - 1999
49   Competition and multiple cause models - Dayan, Zemel - 1995
41   Ensemble learning for hidden Markov models - MacKay - 1997
35   Blind separation of instantaneous mixtures of nonstationary .. - Pham, Cardoso - 2001
31   BUGS: Bayesian inference using Gibbs sampling (context) - Spiegelhalter, Thomas et al. - 1995
28   Multidimensional independent component analysis - Cardoso - 1941
26   Ensemble learning - Lappalainen, Miskin - 2000
25   Expectation propagation for approximate Bayesian inference (context) - Minka - 2001
25   Independent component analysis of natural image sequences yi.. - van Hateren, Ruderman - 1998
24   An unsupervised ensemble learning method for nonlinear dynam.. - Valpola, Karhunen - 2002
23   Bayesian nonlinear independent component analysis by multi-l.. - Lappalainen, Honkela - 2000
22   Developments in probabilistic modelling with neural networks.. - MacKay - 1995
22   Self-organized formation of various invariant-feature filter.. - Kohonen, Kaski et al. - 1997
21   Learning nonlinear dynamical systems using an EM algorithm - Ghahramani, Roweis - 1999
20   Computing Science and Statistics (context) - Murphy, net et al. - 2001
17   Classification by minimum-message-length inference (context) - Wallace - 1990
17   Independent Component Analysis: Principles and Practice (context) - Roberts, Everson - 2001
15   Variational Algorithms for Approximate Bayesian Inference (context) - Beal - 2003
15   Ensemble learning in Bayesian neural networks (context) - Barber, Bishop - 1998
15   A variational approximation for Bayesian networks with discr.. - Murphy - 1999
14   Building blocks for hierarchical latent variable models - Valpola, Raiko et al.
13   Graphical Models: Foundations of Neural Computation (context) - Jordan, Sejnowski - 2001
13   and Learning Algorithms (context) - MacKay, Inference - 2003
12   Variational learning in nonlinear Gaussian belief networks - Frey, Hinton - 1999
10   The variational Bayesian EM algorithm for incomplete data: w.. - Beal, Ghahramani - 2003
10   Hierarchical non-linear factor analysis and topographic maps - Ghahramani, Hinton - 1998
9   Latent variable models (context) - Bishop - 1999
9   Variational learning of clusters of undercomplete nonsymmetr.. - Chan, Lee et al.
9   An ensemble learning approach to independent component analy.. - Choudrey, Penny et al. - 2000
9   Nonlinear independent factor analysis by hierarchical models - Valpola, Ostman et al.
8   Ensemble learning for blind source separation - Miskin, MacKay - 2001
8   Emergence of phase and shift invariant features by decomposi.. (context) - Hyvarinen, Hoyer - 2000
7   Selforganizing map in Matlab: the SOM toolbox - Vesanto, Himberg et al. - 1999
7   Emergence of topography and complex cell properties from nat.. (context) - Hyvarinen, Hoyer - 2000
6   Automatic derivation of statistical algorithms: The EM famil.. - Gray, Fischer et al. - 2002
6   Hierarchical models of variance sources - Valpola, Harva et al. - 2004
5   Higher-order statistical properties arising from the non-sta.. - Parra, Spence et al. - 2001
5   graphical models and variational methods (context) - Attias - 2001
4   Variational message passing (context) - Winn, Bishop - 2005
4   Mean-field approaches to independent component analysis (context) - Hjen-Srensen, Winther et al. - 2002
3   Kalman Filtering and Neural Networks (context) - Haykin - 2001
3   Missing values in hierarchical nonlinear factor analysis - Raiko, Valpola et al. - 2003
3   Bayes Blocks software library (context) - Valpola, Honkela et al. - 2003
3   Speeding up cyclic update schemes by pattern searches (context) - Honkela - 2002
2   An ensemble learning approach to nonlinear dynamic blind sou.. - Valpola, Honkela et al. - 2002
2   Nonlinear blind source separation by variational Bayesian le.. - Valpola, Oja et al. - 2003
2   Multivariate Bayesian Statistics: Models for Source Separati.. (context) - Rowe - 2003
2   A datadriven Bayesian approach to finding young stellar popu.. (context) - Nolan, Harva et al. - 2005
2   Hierarchical Variance Models of Image Sequences (context) - Harva - 2004
2   Variational learning and bits-back coding: an information-th.. (context) - Honkela, Valpola - 2004
2   Unsupervised variational Bayesian learning of nonlinear mode.. (context) - Honkela, Valpola - 2005
2   Accelerating cyclic update algorithms for parameter estimati.. - Honkela, Valpola et al. - 2003
2   A variational Bayesian method for rectified factor analysis (context) - Harva, Kaban - 2005
2   Partially observed values (context) - Raiko - 2004
2   Learning nonlinear state-space models for control (context) - Raiko, Tornio - 2005
1   Empirical evidence of the linear nature of magnetoencephalog.. (context) - Honkela, Ostman et al. - 2005
1   and model pruning in variational free energy minimization (context) - MacKay, symmetry-breaking - 2001
1   Nonlinear relational Markov networks with an application to .. - Raiko - 2005
1   Bayes Blocks: An implementation of the variational Bayesian .. - Harva, Raiko et al. - 2005
1   Nonlinear dynamical factor analysis for state change detecti.. (context) - Ilin, Valpola et al. - 2003
1   Using kernel PCA for initialisation of variational Bayesian .. (context) - Honkela, Harmeling et al. - 2004
1   priors and wavelets: structure and signal modelling using IC.. (context) - Roberts, Roussos et al. - 2004
1   A hierarchical ICA method for unsupervised learning of nonli.. (context) - Park, Lee
1   ect of the form of the posterior approximation in variationa.. (context) - Ilin, Valpola

Documents on the same site (http://www.cis.hut.fi/praiko/):   More
Towards Discovering Structural Signatures of Protein Folds based .. - Kersting (2002)   (Correct)
Building Blocks For Hierarchical Latent Variable Models - Valpola, Raiko, Karhunen (2001)   (Correct)
Constructing Graphical Models for Bayesian Ensemble.. - Harri Valpola Tapani   (Correct)

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC