PPT-4 : Uncertainty and Probability

The scientist has a lot of experience with ignorance and doubt and uncertainty and this experience is of very great importance I think When a scientist does not know the answer to a problem he is ignorant When he has a hunch as to what the result is he is uncertain And when he is pretty damn sure of what the result is going to be he is still in some doubt. sources:. sensory/processing noise. ignorance. change. consequences:. inference. learning. coding:. distributional/probabilistic population codes. neuromodulators. Multisensory Integration. +.  . apply the previous analysis:. Manipulating symbols. Last class. Typology of signs. Sign systems. Symbols. Tremendously important distinctions for informatics and computational sciences. Computation = symbol manipulation. Symbols can be manipulated without reference to content (syntactically. Jake Blanchard. Fall 2010. Uncertainty Analysis for Engineers. 1. Instructor. Jake Blanchard. Engineering Physics. 143 Engineering Research Building. blanchard@engr.wisc.edu. Uncertainty Analysis for Engineers. in Precipitation Data Records. Yudong Tian. Collaborators: Ling Tang, Bob Adler, George Huffman, . Xin Lin, Fang Yan, Viviana Maggioni and Matt Sapiano.  . University of Maryland & NASA/GSFC. http://sigma.umd.edu. Jake Blanchard. Spring 2010. Uncertainty Analysis for Engineers. 1. Introduction. Interpretations of Probability. Classical – If an event can occur in N equally likely and different ways, and if n of these have an attribute A, then the probability of the occurrence of A, denoted Pr(A), is defined as n/N. Making Uncertainty Valuable not Risky. About KCA. Management Consultancy focused on Energy, Technology, and Related Markets.. Work with clients to develop and implement game-changing strategies, improve operational efficiencies, and reduce costs through long-term competitive advantage.. Lack of knowledge vs. variability.. What type of measures do we take to reduce uncertainty?. Design, manufacturing, operations & post-mortems. Living with uncertainties vs. changing them. How do we represent random variables?. OUTLINE. Accelerator Pre-alignment background. Uncertainty and GUM supplement 1. PACMAN pre-alignment budgeting . CMM uncertainty modeling. Thermal uncertainty compensation and modeling. First stochastic modeling results. Accuracy vs. Precision. What is the difference?. Accuracy vs. Precision. Precision . accuracy. Exactness. Divisions on scale. Reproducibility. Uncertainty. Significant digits. Correctness. Calibration. 4. Introduction. (slide 1 of 3). A key . aspect of solving real business problems is dealing appropriately with uncertainty.. This involves recognizing explicitly that uncertainty exists and using quantitative methods to model uncertainty.. Section 1: Observation, Describing quantities, and Estimation. Section 2: Measurement uncertainty, Error propagation, and Analytical methods. Section 1 Objectives. . Core Principles. Physics and Observation. : . Case Study of. . Residential Wood Combustion . Rabab . Mashayekhi, . Shunliu. . Zhao, . Sahar . Saeednooran. , . Amir . Hakami. Department of Civil and Environmental Engineering, Carleton University, . Uncertainty. Irreducible uncertainty . is inherent to a system. Epistemic uncertainty . is caused by the subjective lack of knowledge by the algorithm designer. In optimization problems, uncertainty can be represented by a vector of random variables . Richard J. Nugent III, Ph.D. . Economist, HEC-FDA Lead. Hydrologic Engineering Center . Davis, CA. agenda. What is flood risk, and how do we describe it?. How do we assess flood risk?. What goes into the uncertainty about flood risk?.