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 . Let be a conditional distribution for given the unknown parameter For the observed data the function considered as a function of is called the likelihood function The name likelihood implies that given the value of is more likely to be the tr 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 ?. 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. 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 ?. Post-mortem. Project presentations in the last 2-3 classes. Start of Statistical Learning. Sanity Test... Max: 52.5 Min: 6 . Avg. : 24.6 . Stdev. : 14.8 . Including those sitting-in: . Avg. (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. Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course. London, May 11, 2015. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Thinking and Everyday Life. Michael K. Tanenhaus. Inference in an uncertain world. Most of what we do, whether consciously or unconsciously involves probabilistic inference. Decisions. Some are conscious:. 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. Hypothesis Testing in Binary Choice Models. Hypothesis Tests. Restrictions: Linear or nonlinear functions of the model parameters. Structural ‘change’: Constancy of parameters. Specification Tests: . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course. London, May 12, 2014. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Mathys. Wellcome Trust Centre for Neuroimaging. UCL. London SPM Course. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Machine Learning. Chapter 2: Probability distributions. Parametric Distributions. Basic building blocks:. Need to determine given . Representation: or ?. Recall Curve Fitting.