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Neal, R. M. (1993). Probabilistic Inference Using Markov Chain Monte Carlo Methods (Technical Report) . Department of Computer Science, University of Toronto.

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Efficient Feature Construction by Meta Learning - Guiding the .. - Mierswa, Wurst (2005)   (Correct)

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Neal, R. M. (1993). Probabilistic Inference Using Markov Chain Monte Carlo Methods (Technical Report) . Department of Computer Science, University of Toronto.


A Non-Parametric Bayesian Approach to Spike Sorting - Frank Wood Sharon (2006)   (Correct)

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R. M. Neal, "Probabilistic inference using Markov chain Monte Carlo methods," University of Toronto, Tech. Rep. CRG-TR-93-1, 1993.


Variational methods for the Dirichlet process - Blei, Jordan (2004)   (1 citation)  (Correct)

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Neal, R. (1993). Probabilistic inference using Markov chain Monte Carlo methods (Technical Report CRG-TR-93-1). Department of Computer Science, University of Toronto.


Fields of Experts: A Framework for Learning Image Priors - Roth, Black (2005)   (Correct)

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R. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 1993.


The Prior-Predictive Value: - Paradigm Of Nasty   (Correct)

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R. Neal, "Probabilistic inference using markov chain monte carlo methods," Dept. of Computer Science, University Toronto, 1993.


On Evidence Weighted Mixture Classification - Everson, Krzanowski, Bailey..   (Correct)

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R.M. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 1993.


Graphical Models for Statistical Inference and Data.. - Ihler, Kirshner.. (2005)   (Correct)

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R. M. Neal, Probabilistic inference using Markov Chain Monte Carlo methods, Tech. Rep. CRG-TR-93-1, Dept. of Comp. Sci., Univ. of Toronto (1993).


Bayesian Learning in Undirected Graphical Models.. - Murray, Ghahramani (2004)   (3 citations)  (Correct)

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Radford M. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical report, Department of Computer Science, University of Toronto, September 1993.


Efficient Feature Construction by Meta Learning - Guiding the .. - Mierswa, Wurst (2005)   (Correct)

No context found.

Neal, R. M. (1993). Probabilistic Inference Using Markov Chain Monte Carlo Methods (Technical Report) . Department of Computer Science, University of Toronto.


Probabilistic Independence Networks for Hidden Markov.. - Smyth, Heckerman, al. (1996)   (91 citations)  (Correct)

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Neal, R. 1993. Probabilistic inference using Markov chain Monte Carlo methods. CRGTR -93-1, Department of Computer Science, University of Toronto.


World Independent Context Pair Classification Model for . . . - Niu, Al. (2005)   (Correct)

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Neal, R.M. 1993. Probabilistic Inference Using Markov Chain Monte Carlo Methods. Technical Report, Univ. of Toronto.


Learning Dynamic Bayesian Networks - Zoubin Ghahramani Department (1997)   (39 citations)  (Correct)

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R. M. Neal. Probabilistic inference using Markov chain monte carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, 1993.


Fields of Experts: A Framework for Learning Image Priors - Roth, Black (2005)   (Correct)

No context found.

R. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 1993.


Reinforcement Learning for Factored Markov Decision Processes - Sallans (2002)   (Correct)

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Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto.


Bayesian Learning in Nonlinear State-Space Models - Andrews   (Correct)

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Neal, R. M. (1993), Probabilistic inference using markov chain monte carlo methods, Technical report, University of Toronto.


The Prior-Predictive Value: A Paradigm of Nasty.. - von der Linden.. (1999)   (Correct)

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R. Neal, "Probabilistic inference using markov chain monte carlo methods," Dept. of Computer Science, University Toronto, 1993.


Distributed Clustering with Limited Knowledge Sharing - Ghosh, Merugu   (Correct)

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R. M. Neal. Probabilistic inference using Markov Chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, Univ. of Toronto, 1993.


Bayesian Learning in Undirected Graphical Models.. - Murray, Ghahramani (2004)   (3 citations)  (Correct)

No context found.

Radford M. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical report, Department of Computer Science, University of Toronto, September 1993.


Linearly scalable hybrid Monte Carlo method for.. - Hampton, Izaguirre (2002)   (Correct)

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R. M. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, university of Toronto, 1993. papers available from http://www.cs.toronto.edu/radford/papers-online.html.


The design and implementation of a Bayesian CAD modeler for - Robotic Applications..   (Correct)

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Neal, R. M. (1993). Probabilistic inference using Markov Chain Monte Carlo methods. Research Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto.


A Probabilistic Approach to Privacy-sensitive Distributed.. - Srujana Merugu And (2003)   (1 citation)  (Correct)

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R. M. Neal. Probabilistic inference using Markov Chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, Univ. of Toronto, 1993.


Improved Sampling of Configuration Space of Biomolecules Using.. - Hampton (2004)   (Correct)

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R. M. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, University of Toronto, 1993.


Shadow Hybrid Monte Carlo: An Efficient Propagator in Phase .. - Izaguirre, Hampton (2004)   (Correct)

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R. M. Neal, Probabilistic inference using Markov chain Monte Carlo methods, Tech. Rep. CRG-TR-93-1, University of Toronto (1993).


Privacy-preserving Distributed Clustering using Generative.. - Srujana Merugu And (2003)   (4 citations)  (Correct)

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R. M. Neal. Probabilistic inference using Markov Chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 1993.


Regularized Greedy Importance Sampling - Finnegan Southey Dale   (Correct)

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R. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Tech report, 1993.

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