Chapter 1 Approximate inference for continuous-time Markov processes C´edric Archambeau1 and Manfred Opper2 1.1

Source URL: www0.cs.ucl.ac.uk

Course Description: Bayesian methodology and (Markov chain) Monte Carlo simulation for financial applications 3 ECTS Professor Antonietta Mira

Source URL: www.istfin.eco.usi.ch

• Statistics
• Statistical theory
• Monte Carlo methods
• Markov models
• Markov chain Monte Carlo
• Computational statistics
• Statistical mechanics
• Bayesian network
• Bayesian inference
• Approximate Bayesian computation
• Bayesian statistics
• MetropolisHastings algorithm

Probability Type Inference for Flexible Approximate Programming Brett Boston Adrian Sampson

Source URL: adriansampson.net

Building blocks for exact and approximate inference Jacques Carette McMaster University Praveen Narayanan

Source URL: www.blackboxworkshop.org

Tuning Approximate Computations with Constraint-Based Type Inference Brett Boston Adrian Sampson

Source URL: sampa.cs.washington.edu

Learning Efficiently with Approximate Inference via Dual Losses Ofer Meshi David Sontag Tommi Jaakkola Amir Globerson

Source URL: cs.nyu.edu

A NOVEL APPROXIMATE INFERENCE APPROACH TO AUTOMATED CLASSIFICATION OF PROTEIN SUBCELLULAR LOCATION PATTERNS IN MULTI-CELL IMAGES Shann-Ching Chen1, Geoffrey J. Gordon3, and Robert F. Murphy1,2,3 Department of Biomedical

Source URL: murphylab.web.cmu.edu

Sublinear Approximate Inference for Probabilistic Programs arXiv:1411.1690v1 [stat.ML] 6 NovYutian Chen1 , Vikash Mansinghka2 and Zoubin Ghahramani1

Source URL: arxiv.org

Approximate Inference in Graphical Models using LP Relaxations by David Alexander Sontag Submitted to the Department of Electrical Engineering and Computer Science

Source URL: cs.nyu.edu

Approximate Inference in Probabilistic Graphical Models with Determinism Vibhav Gogate Donald Bren School of Information and Computer Science University of California, Irvine, CA 92697, USA

Source URL: www.hlt.utdallas.edu