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IJCAI
1997
13 years 11 months ago
A Study of Causal Discovery With Weak Links and Small Samples
Weak causal relationships and small sample size pose two significant difficulties to the automatic discovery of causal models from observational data. This paper examines the infl...
Honghua Dai, Kevin B. Korb, Chris S. Wallace, Xind...
GRAPHICSINTERFACE
2003
13 years 11 months ago
Entropy-based Adaptive Sampling
Ray tracing techniques need supersampling to reduce aliasing and/or noise in the final image. Since not all the pixels in the image require the same number of rays, supersampling...
Jaume Rigau, Miquel Feixas, Mateu Sbert
JMLR
2006
118views more  JMLR 2006»
13 years 10 months ago
Learning Factor Graphs in Polynomial Time and Sample Complexity
We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...
Pieter Abbeel, Daphne Koller, Andrew Y. Ng
AI
2007
Springer
14 years 4 months ago
Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks
Importance sampling-based algorithms are a popular alternative when Bayesian network models are too large or too complex for exact algorithms. However, importance sampling is sensi...
Changhe Yuan, Marek J. Druzdzel
SIGMOD
2004
ACM
118views Database» more  SIGMOD 2004»
14 years 10 months ago
Effective Use of Block-Level Sampling in Statistics Estimation
Block-level sampling is far more efficient than true uniform-random sampling over a large database, but prone to significant errors if used to create database statistics. In this ...
Surajit Chaudhuri, Gautam Das, Utkarsh Srivastava