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...
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...
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...
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...
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 ...