'Empirical distribution' presentation slideshows

Maximum Entropy Model (I)

990 views • 80 slides

Maximum Entropy Model (I)

1.33k views • 77 slides

Seeing Patterns in Randomness: Irrational Superstition or Adaptive Behavior?

688 views • 52 slides

GENERATING STOCHASTIC VARIATES

GENERATING STOCHASTIC VARIATES . we discuss techniques for generating random numbers with a specific distribution Random numbers following a specific distribution are called random variates or stochastic variates. The inverse transformation method .

582 views • 39 slides

Examining Clumpiness in FPS

Examining Clumpiness in FPS. David K. Walters Roseburg Forest Products. Background. Clumpiness - described as the degree to which the trees on a given acre are dispersed in a less than uniform fashion Example,

473 views • 38 slides

CS433: Modeling and Simulation

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CS433 Modeling and Simulation Lecture 05 Statistical Analysis Tools

565 views • 35 slides

Convergence concepts in probability theory

Convergence concepts in probability theory. Definitions and relations between convergence concepts Sufficient conditions for almost sure convergence Convergence via transforms The law of large numbers and the central limit theorem. Coin-tossing: relative frequency of heads.

1.04k views • 29 slides

Learning Random Walk Models for Inducing Word Dependency Distributions

394 views • 27 slides

Market Risk VaR: Historical Simulation Approach N. Gershun

Market Risk VaR: Historical Simulation Approach N. Gershun. Historical Simulation. Collect data on the daily movements in all market variables. The first simulation trial assumes that the percentage changes in all market variables are as on the first day

603 views • 26 slides

Risk Aversion and Expected Utility Theory: A Field Experiment with Large and Small Stakes

Risk Aversion and Expected Utility Theory: A Field Experiment with Large and Small Stakes. Matilde Bombardini (UBC) Francesco Trebbi (University of Chicago GSB). Motivation. How well does expected utility theory explain the behavior of an average person?

430 views • 25 slides

Portfolio Analyzer and Risk Stationarity Lecture 23

Portfolio Analyzer and Risk Stationarity Lecture 23. Read Chapters 13 and 14 Lecture 23 Portfolio Analyzer Example.xlsx Lecture 23 Portfolio Analyzer 2016.XLSX Lecture 23 Portfolio Low Correlation.XLSX Lecture 23 Portfolio High Correlation.XLSX Lecture 23 Changing Risk Over Time.XLSX

353 views • 23 slides

Modeling Banking, Soverign, and MacroRisk in a CCA Global VAR

Modeling Banking, Soverign, and MacroRisk in a CCA Global VAR. Discussion by Ravi Jagannathan New York, 31 May 2014. CCA VAR. VARs Work-horse of economists

319 views • 21 slides

Towards Scaling Fully Personalized PageRank

359 views • 16 slides

Smoothing/Priors/ Regularization for Maxent Models

Smoothing/Priors/ Regularization for Maxent Models. Smoothing: Issues of Scale. Lots of features: NLP maxent models can have well over a million features. Even storing a single array of parameter values can have a substantial memory cost. Lots of sparsity :

306 views • 15 slides

Variable Threshold Methods for Functional Neuroimaging

Variable Threshold Methods for Functional Neuroimaging. Thomas Nichols, Ph.D. Department of Biostatistics U. of Michigan Randy Buckner, Ph.D. Department of Psychology, Washington U., St. Louis. Introduction.

282 views • 15 slides

Analysis of Simulation Input

Analysis of Simulation Input. Simulation Machine. Simulation can be considered as an Engine with input and output as follows:. Simulation Engine. Output. Input. Realizing Simulation. Input Analysis: is the analysis of the random variables involved in the model such as:

315 views • 15 slides

Statistical analysis and modeling of neural data Lecture 7

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ACTSC 625 CAS and Health Insurance Mathematics

249 views • 12 slides

Bootstrap - Example

Bootstrap - Example. Suppose we have an estimator of a parameter and we want to express its accuracy by its standard error but its sampling distribution is too complicated to derive theoretically.

463 views • 6 slides