'Continuous distributions' diaporamas de présentation

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Topic 6: Probability

Topic 6: Probability. Dr J Frost (jfrost@tiffin.kingston.sch.uk ) www.drfrostmaths.com. Last modified: 31 st August 2015. Slide guidance. Key to question types:. SMC. Senior Maths Challenge. Uni. University Interview. Questions used in university interviews (possibly Oxbridge).

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A Refresher on Probability and Statistics

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Chapter 7

Chapter 7. Continuous Distributions. Continuous random variables. Are numerical variables whose values fall within a range or interval Are measurements Can be described by density curves. Density curves. Is always on or above the horizontal axis

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Chapter 11: Selected Quantitative Relationships (pt. 1)

Chapter 11: Selected Quantitative Relationships (pt. 1). ISE 443 / ETM 543 Fall 2013. The development of large, complex systems involves the use of quantitative relationships. To predict system performance before it is built response times success rates system availability

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Distributions

Distributions. Continuous Distributions. Continuous random variables. Are numerical variables whose values fall within a range or interval Are measurements Can be described by density curves. Density curves. Is always on or above the horizontal axis

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Final Review

Final Review. Econ 240A. Outline. The Big Picture Processes to remember ( and habits to form) for your quantitative career (FYQC) Concepts to remember FYQC Discrete Distributions Continuous distributions Central Limit Theorem Regression. The Classical Statistical Trail. Rates &

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Use of moment generating functions

Use of moment generating functions . Definition. Let X denote a random variable with probability density function f ( x ) if continuous (probability mass function p ( x ) if discrete) Then m X ( t ) = the moment generating function of X.

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Simulation

Simulation. Summer 2013. Many definitions. It is the process of studying the behavior of a real system using a computer-based model that replicates the behavior of that system. Used in situations involving probabilistic elements (e.g. random arrivals , service times and process yields)

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A Refresher on Probability and Statistics

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A Refresher on Probability and Statistics

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Pseudo Random Numbers

Pseudo Random Numbers. Random numbers generated by a computer are not really random They just behave like random numbers For a large enough sample, the generated values will pass all tests for a uniform distribution If you look at a histogram of a large number, it will look uniform

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9.4 Random Numbers from Various Distributions

9.4 Random Numbers from Various Distributions. Distributions. A distribution of numbers is a description of the portion of times each possible outcome or range of outcomes occurs on average. A histogram is a display of a given distribution.

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Lecture 7

Lecture 7. Dan Piett STAT 211-019 West Virginia University. Last Week. Binomial Distributions 2 Outcomes, n trials, probability of success = p, X = Number of Successes Poisson Distributions Occurrences are measured over some unit of time/space with mean occurrences lambda

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THE EXPONENTIAL GARCH MODEL

THE EXPONENTIAL GARCH MODEL. THE EXPONENTIAL GARCH MODEL. To allow for asymmetric effects between positive and negative asset returns, he considers the weighted innovation where ? and ? are real constants

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Some standard univariate probability distributions

Some standard univariate probability distributions. Characteristic function, moment generating function, cumulant generating functions Discrete distribution Continuous distributions Some distributions associated with normal References.

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Unit 5: Modelling Continuous Data

Unit 5: Modelling Continuous Data. Lesson 1: Continuous Probability Distributions. Introduction. The binomial distribution from last unit dealt with discrete random variables, which were always represented with a whole number (e.g. number of heads.)

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Discrete Distributions

Discrete Distributions . The values generated for a random variable must be from a finite distinct set of individual values. For example, based on past observations, the annual demand for the FOT-320 is a discrete random variable that is limited to positive integer values in a certain range.

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Random-Number Generators (RNGs)

Random-Number Generators (RNGs). Algorithm to generate independent, identically distributed draws from the continuous UNIF (0, 1) distribution These are called random numbers in simulation Basis for generating observations from all other distributions and random processes

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