40 search results - page 3 / 8 » Monte Carlo Hidden Markov Models: Learning Non-Parametric Mo... |
UAI
2004
13 years 6 months ago
2004
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
AI
2006
Springer
13 years 5 months ago
2006
Springer
In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behaviour...
NIPS
2008
13 years 6 months ago
2008
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
JAIR
2002
13 years 4 months ago
2002
Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
IJCAI
2007
13 years 6 months ago
2007
We present a general machine learning framework for modelling the phenomenon of missing information in data. We propose a masking process model to capture the stochastic nature of...