'Negative binomial distribution' presentation slideshows

Discrete Random Variables

Discrete Random Variables. Discrete random variables. For a discrete random variable X the probability distribution is described by the probability function, p ( x ), which has the following properties :. Comment:.

1k views • 83 slides

Discrete Random Variables

Discrete Random Variables. Discrete random variables. For a discrete random variable X the probability distribution is described by the probability function, p ( x ), which has the following properties :. Comment:.

864 views • 83 slides

Random Variables

559 views • 35 slides

Queuing Theory Operational Research-Level 4

Queuing Theory Operational Research-Level 4. Prepared by T.M.J.A. Cooray Department of Mathematics. Introduction. Markov processes in continuous time was considered in the previous section.

474 views • 30 slides

Outline of the talk

Getting rid of stochasticity (applicable sometimes) Han Hoogeveen Universiteit Utrecht Joint work with Marjan van den Akker. Outline of the talk. Problem description How to solve the deterministic problem Stochastic processing times consequences four classes of instances

396 views • 30 slides

Outline of the talk

Getting rid of stochasticity (applicable sometimes) Han Hoogeveen Universiteit Utrecht Joint work with Marjan van den Akker. Outline of the talk. Problem description How to solve the deterministic problem Stochastic processing times consequences four classes of instances

464 views • 30 slides

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Chapter 3 Some Special Distributions

Chapter 3 Some Special Distributions. Math 6203 Fall 2009 Instructor: Ayona Chatterjee. 3.1 The Binomial and Related Distributions. Bernoulli Distribution.

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PHYS 575/675

PHYS 575/675. Introduction to Statistics and Error Analysis. Modern Detectors. Outline. Introduction Treatment of experimental data Probability distributions Maximum likelihood Parameter estimation Method of least squares Bayesian approach Hypothesis and significance testing

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Multinomial Experiments

Multinomial Experiments. What if there are more than 2 possible outcomes? (e.g., acceptable, scrap, rework) That is, suppose we have: n independent trials k outcomes that are mutually exclusive (e.g., ?, ?, ?, ?) exhaustive (i.e., ? all k p i = 1) Then

628 views • 20 slides

A Survey of Multiplicity Fluctuations in PHENIX

Quark Matter 2005 Jeffery T. Mitchell (Brookhaven National Laboratory) for the PHENIX Collaboration. A Survey of Multiplicity Fluctuations in PHENIX. OUTLINE Scaled Variance vs. Centrality Negative Binomial Distribution Fits: k Parameter vs. Centrality HIJING comparisons

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Setting the neighborhood on fire

251 views • 13 slides

Count Data

Count Data. Harry R. Erwin, PhD School of Computing and Technology University of Sunderland. Resources. Crawley, MJ (2005) Statistics: An Introduction Using R. Wiley. Freund, RJ, and WJ Wilson (1998) Regression Analysis, Academic Press.

203 views • 10 slides

Probability Generating Functions

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Probability Generating Functions

435 views • 6 slides