1 / 8

Lec . 08 – Discrete (and Continuous) Probability Distributions

Lec . 08 – Discrete (and Continuous) Probability Distributions. Independence. Discrete Uniform Distribution. What are some examples of this?. Binomial Distribution.

randi
Télécharger la présentation

Lec . 08 – Discrete (and Continuous) Probability Distributions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lec. 08 – Discrete (and Continuous) Probability Distributions

  2. Independence

  3. Discrete Uniform Distribution What are some examples of this?

  4. Binomial Distribution If interested in obtaining the probability of r successes out of n trials over a range of r, when the probability is known – see our first example of the course!

  5. A Poisson experiment is a statistical experiment that has the following properties: The experiment results in outcomes that can be classified as successes or failures. The average number of successes (μ) that occurs in a specified region is known. Probability that a success will occur is proportional to the size of the region. Probability that a success will occur in an extremely small region is virtually zero. Poisson Distribution

  6. First, code up the Poisson distribution for a mean of your choosing, and display the histogram. Write a MATLAB code to answer the following questions about floods: Poisson Distribution Example

  7. Binomial = Distribution of the number of successes in a fixed number of trials Negative Binomial = Distribution of the minimum number of trials required to produce a fixed number of successes (e.g. number of wells drilled to find 3 exploitable reservoirs) Geometric distribution – simplest form – defines prob. distrib. of trials needed to obtain the 1st success: Pr(X=x)=(1-p)x-1p Prob. of number of trials required to obtain exactly r successes: Negative Binomial Distribution

  8. Continuous Random Variables = p.d.f.’s Poisson Distribution (discrete)  Exponential Distribution (continuous)

More Related