'Random variate' presentation slideshows

Random Variate Generation

Random Variate Generation. It is assumed that a distribution is completely specified and we wish to generate samples from this distribution as input to a simulation model. Techniques Inverse Transformation* Acceptance-Rejection Convolution will emphasize. Random Variate Generation (cont.).

981 views • 44 slides

Body size distribution of European Collembola

Lecture 9 Moments of distributions. Body size distribution of European Collembola. Body size distribution of European Collembola. Modus. The histogram of raw data. Three Collembolan weight classes. What is the average body weight ? . Sample mean. Population mean.

413 views • 30 slides

418 views • 28 slides

CS433 Modeling and Simulation Lecture 01 Introduction

607 views • 25 slides

Lecture 25 Simulation and Modeling

Lecture 25 Simulation and Modeling. Md. Tanvir Al Amin, Lecturer, Dept. of CSE, BUET. CSE 411. Recap : Inverse Transform Method. Find cumulative distribution function Let it be F Find F -1 X = F -1 (R) is the desired variate where R ~ U(0,1) Other methods are Accept Reject technique

332 views • 24 slides

CPSC 531: System Modeling and Simulation

CPSC 531: System Modeling and Simulation. Carey Williamson Department of Computer Science University of Calgary Fall 2017. Recap and Terminology. (Pseudo-) Random Number Generation (RNG) A fundamental primitive required for simulations Goal: Uniform(0,1) Uniformity Independence

255 views • 24 slides

Random Variate Generation

Random Variate Generation. Overview. Inverse transformation Rejection Composition Convolution Characterization. Random-Variate Generation. General Techniques Only a few techniques may apply to a particular distribution Look up the distribution in Chapter 29. 1.0. u. CDF F(x). 0.5.

1.14k views • 21 slides

Random Variate Generation

Random Variate Generation. Overview. Inverse transformation Rejection Composition Convolution Characterization. Random-Variate Generation. General Techniques Only a few techniques may apply to a particular distribution Look up the distribution in Chapter 29. 1.0. u. CDF F(x). 0.5.

483 views • 21 slides

Probabilistic Modeling and Uncertainty

Probabilistic Modeling and Uncertainty. Representation of uncertainty Uncertainty ? Lack of knowledge Uncertainty represented as lack of determinism; stochastic or random in contrast with fixed What might be represented as random phenomena? Arrivals of passengers to an airport

294 views • 16 slides

Random- Variate Generation

Random- Variate Generation. Andy Wang CIS 5930-03 Computer Systems Performance Analysis. Random- Variate Generation. Methods to generate nonuniform variables Each method is applicable to a subset of the distribution For a distribution, one method may be more efficient than the others.

265 views • 13 slides