PPT-HUDM4122 Probability and Statistical Inference

April 1 2015 First Announcement HW8 will be due on April 15 rather than April 13 I dont expect us to get through the entire lecture today so I decided to delay the homework rather than splitting it. Prof. Tudor Dumitraș. Assistant Professor, ECE. University of Maryland, College Park. ENEE 759D | ENEE 459D | CMSC . 858Z. http://ter.ps/. 759d . https://www.facebook.com/SDSAtUMD. Today’s Lecture. k. -partite graphs. Sarah . Gester. , . Ermir. . Qeli. , Christian H. Ahrens, and Peter . Buhlmann. Problem Description. Given peptides and scores/probabilities, infer the set of proteins present in the sample.. Kathryn Blackmond Laskey. Department of Systems Engineering and Operations Research. George Mason University. Dagstuhl. Seminar April 2011. The problem of plan recognition is to take as input a sequence of actions performed by an actor and to infer the goal pursued by the actor and also to organize the action sequence in terms of a plan structure. Sergio Pissanetzky. Sergio@SciControls.com. Emergent Inference. Any system. VISION. ROBOT. SOFTWARE. your mom. grab. an. object. computer. program. eyes. cameras,. sensors. translation. 100,000,000. 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. Tropical Cyclone Forecasting. . Mark DeMaria, NOAA/NCEP/NHC. Temporary Duty Station, . Fort Collins, CO. HWRF Tutorial, College Park, MD. Januar. y 14, . 2014. 1. Outline. Overview of statistical techniques for tropical cyclone forecasting . 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:. 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. Stat-GB.3302.30, UB.0015.01. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Statistical Inference and Regression Analysis. Part 0 - Introduction. . Professor William Greene; Economics and IOMS Departments. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). February 18, 2015. HW. Getting harder…. HW. Getting harder…. Difficulties. A lot of trouble with sample space calculation. A reminder. The sample space is the total number of combination of things that can happen. Chapter 19 . Temporal models. 2. Goal. To track object state from frame to frame in a video. Difficulties:. Clutter (data association). One image may not be enough to fully define state. Relationship between frames may be complicated. February 11, 2015. In the last class. Basic Introduction to Probability. In the last class. 10 heads in a row means Ryan has a biased coin. Computing probabilities from sample spaces. Reprised Example. (and how to avoid them) . Conflict of Interest Disclosure. I have no potential conflict of interest to report. A quick tour of common statistical errors. Advice to help your submission pass statistical review.