PPT-Modern Likelihood-Frequentist Inference

Donald A Pierce Emeritus OSU Statistics and Ruggero Bellio Univ of Udine Slides and working paper other things are at httpwwwscienceoregonstateedu piercedo Slides and paper only are at . Frequentist Bayesian Fullinformation iteratedltering(mif) PMCMC(pmcmc) Feature-based nonlinearforecasting(nlf) ABC(abc) probematching&synthetic likelihood(probe.match) (b)Notplug-and-play Frequentist Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course (M/EEG). London, May 14, 2013. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Lecture XX. Reminder from Information Theory. Mutual Information: . . Conditional Mutual Information: . . Entropy: Conditional Mutual Information: . . Scoring Maximum Likelihood Function. When scoring function is the Maximum Likelihood, the model would make the data as probable as possible by choosing the graph structure that would produce the highest score for the MLE estimate of the parameter, we define:. Maybe I am giving you too much food for thought on this one issue. The realization comes after reading a preprint by a >100-strong collaboration, MINOS. In a recent paper [MINOS 2011] they derive 99.7% upper limits for some parameters, using antineutrino interactions. How then to combine with previous upper limits derived with neutrino interactions ?. (Markov Nets). (Slides from Sam . Roweis. ). Connection to MCMC:. . . MCMC requires sampling a node given its . markov. blanket. . Need to use P(. x|MB. (x)). . . For . Bayes. nets MB(x) contains more. See Davison Ch. 4 for background and a more thorough discussion.. Sometimes. See last slide for copyright information. Maximum Likelihood. Sometimes. Close your eyes and differentiate?. Simulate Some Data: True α=2, β=3. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. Published courtesy of the CEM . FOAMed. Network. http://. www.cemfoamed.co.uk. /portfolio/diagnostics-in-. em. /. Everything we do in a patient assessment is a test. Including questions we ask. Test thresholds. Susan Athey, Stanford GSB. Based on joint work with Guido Imbens, Stefan Wager. References outside CS literature. Imbens and Rubin Causal Inference book (2015): synthesis of literature prior to big data/ML. b. -values for Three Different Tectonic Regimes. Christine . Gammans. What is the . b. -value and why do we care?. Earthquake occurrence per magnitude follows a power law introduced by Ishimoto and Iida (1939) and Guten. Model Definition. Comparison to Bayes Nets. Inference techniques. Learning Techniques. A. B. C. D. Qn. : What is the. . most likely. . configuration of A&B?. Factor says a=b=0. But, marginal says. CSE . 4309 . – Machine Learning. Vassilis. . Athitsos. Computer Science and Engineering Department. University of Texas at . Arlington. 1. Estimating Probabilities. In order to use probabilities, we need to estimate them.. May 29 – June 2, 2017. Fort Collins, Colorado. Instructors:. Charles Canham. And. Patrick Martin. Daily Schedule. Morning. 8:30 – 9:30 Lecture. 9:30 – 10:30 Case Study and Discussion. 10:30 – 12:00 Lab. Zhiyao Duan ¹ & David Temperley ². Department of Electrical and Computer Engineering. Eastman School of Music. University of Rochester. Presentation at ISMIR 2014. Taipei, Taiwan. October 28, 2014.