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IJCAI
2001
13 years 10 months ago
Approximate inference for first-order probabilistic languages
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Hanna Pasula, Stuart J. Russell
WSC
1998
13 years 10 months ago
Bayesian Model Selection when the Number of Components is Unknown
In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
Russell C. H. Cheng
UAI
1996
13 years 10 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
TASLP
2002
109views more  TASLP 2002»
13 years 8 months ago
Particle methods for Bayesian modeling and enhancement of speech signals
This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution mod...
Jaco Vermaak, Christophe Andrieu, Arnaud Doucet, S...
PAMI
2010
124views more  PAMI 2010»
13 years 3 months ago
Structural Approach for Building Reconstruction from a Single DSM
We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a li...
Florent Lafarge, Xavier Descombes, Josiane Zerubia...