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» Aggregation-based model reduction of a Hidden Markov Model
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JMLR
2010
157views more  JMLR 2010»
14 years 11 months ago
Why are DBNs sparse?
Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hi...
Shaunak Chatterjee, Stuart Russell
PERCOM
2007
ACM
16 years 4 months ago
Sensor Scheduling for Optimal Observability Using Estimation Entropy
We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process ...
Mohammad Rezaeian
174
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CORR
2010
Springer
168views Education» more  CORR 2010»
15 years 2 months ago
Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by...
Ricardo Silva
SIGIR
2005
ACM
15 years 10 months ago
Generic soft pattern models for definitional question answering
This paper explores probabilistic lexico-syntactic pattern matching, also known as soft pattern matching. While previous methods in soft pattern matching are ad hoc in computing t...
Hang Cui, Min-Yen Kan, Tat-Seng Chua
TCBB
2011
14 years 11 months ago
Semantics and Ambiguity of Stochastic RNA Family Models
Stochastic models such as hidden Markov models or stochastic context free grammars can fail to return the correct, maximum likelihood solution in the case of semantic ambiguity. T...
Robert Giegerich, Christian Höner zu Siederdi...