Slice sampling - PowerPoint PPT Presentation

Inference

Inference. Overview. The MC-SAT algorithm Knowledge-based model construction Lazy inference Lifted inference. MCMC: Gibbs Sampling. state ← random truth assignment for i ← 1 to num-samples do for each variable x sample x according to P( x | neighbors ( x ))

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Sound and Efficient Inference with Probabilistic and Deterministic Dependencies

Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. Hoifung Poon Dept. Computer Science &amp; Eng. University of Washington (Joint work with Pedro Domingos). Reasoning w. Probabilistic and Deterministic Dependencies. Crucial for many real-world problems

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446 views • 24 slides

Slice Sampling

Slice Sampling. Radford M. Neal The Annals of Statistics (Vol. 31, No. 3, 2003). Introduction. Sampling from a non-standard distribution Metropolis algorithm is sensitive to choice of proposal distribution Proposing changes that are too small leads to inefficient random walk

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495 views • 20 slides

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