PPT-H-likelihood approach to

highdimensional multiple test 28 March 2015 London UK Youngjo Lee Seoul National University w ith Jan F Bj ϕ rnstad Donghwan Lee Peirong Xu Chris Frost Gerard R Ridgway. or. Common . Statistical . Mistakes. . i. n . the Astronomical Literature. Eric Feigelson. Penn State University. Arcetri. Observatory, April 2014. The problem. Astronomers are well-trained in the mathematics underlying physics, but not in applied fields associated with statistical methodology. . Spatial Regression Modeling. GISPopSci. Day 5. Paul R. Voss. and. Katherine J. Curtis. GISPopSci. Review of yesterday. Dealing with heterogeneity in relationships across space. Discrete & continuous spatial heterogeneity in relationships. after . your . seminar . at . Monash. . yesterday I speculated . on how well . 4-out-of-5. . confirming experiments can be considered to support a hypothesis. . Matt . Coller. . School . of Geography and Environmental Science. Corpora and Statistical Methods – Part 2. Preliminaries: Hypothesis testing and the binomial distribution. Permutations. Suppose we have the 5 words {the, dog, ate, a, bone}. How many permutations (possible orderings) are there of these words?. Lobster Survival by Size in a Tethering Experiment. Source: E.B. Wilkinson, J.H. Grabowski, G.D. Sherwood, P.O. . Yund. (2015). "Influence of Predator Identity on the Strength of Predator Avoidance Response in Lobsters," Journal of Experimental Biology and Ecology, Vol. 465, pp. 107-112.. 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. Machine Learning. Last Time. Support Vector Machines. Kernel Methods. Today. Review . of Supervised Learning. Unsupervised . Learning . (. Soft) K-means clustering. Expectation Maximization. Spectral Clustering. Lecture . 5. Pairs . T. rading by Stochastic Spread Methods. Haksun Li. haksun.li@numericalmethod.com. www.numericalmethod.com. Outline. First passage time. Kalman. filter. Maximum likelihood estimate. 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:. Maximum. Likelihood. Estimation. Probabilistic. Graphical. Models. Learning. Biased Coin Example. Tosses are independent of each other. Tosses are sampled from the same distribution (identically distributed). . 1. Elaboration and routes to persuasion. . 2. Factors influencing degree of elaboration. . . A. Elaboration motivation. . . B. Elaboration ability. . . . . . 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. Syllabus. Lecture 01 Describing Inverse Problems. Lecture 02 Probability and Measurement Error, Part 1. Lecture 03 Probability and Measurement Error, Part 2 . Lecture 04 The L. 2. Norm and Simple Least Squares. 0020406081050709Erosion widthdepth ratio0020406081080911112LikelihoodSediment flow factor00204060812878128178LikelihoodD50mm00204060810010203LikelihoodPorosity 0020406081192123LikelihoodDensity kN/m30