1013 search results - page 39 / 203 » The Bayesian inference of phase |
NIPS
2007
13 years 10 months ago
2007
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
ESOP
2011
Springer
13 years 17 days ago
2011
Springer
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
IROS
2007
IEEE
14 years 3 months ago
2007
IEEE
Abstract— Real-world robotic environments are highly structured. The scalability of planning and reasoning methods to cope with complex problems in such environments crucially de...
ICONIP
2007
13 years 10 months ago
2007
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
IJCAI
1997
13 years 10 months ago
1997
Bayesian belief networks have grown to prominence because they provide compact representations of many domains, and there are algorithms to exploit this compactness. The next step...