How+To+Calculate+Distribution+Parameters

How to Calculate Distribution Parameters (Distribution Theory) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Nomenclature | Concepts

See Lecture 2, Slides 46 and 47 for the Functional Forms, E{X} and V[X]. p = probability || Probablly given; just use || m = total successes N = Total Total || Successes: k drawn, m-k not drawn, m total Failures: n-k drawn, N+k-n-m not drawn, N-m total Totals: n drawn, not drawn N-n, N total total. Solve for n, m and N from data given? || b = ending x value || Remember uniform is constant. Mean is numberic average. || beta = ~scale~ || Wikipedia k and theata. Theta is 1/beta. Example on how for mean using kernel and constant is in Lecture 2, Example 13 beginning slide 49. Example how to solve for alpha and beta using 2 linerar equations is Lecture 3, Slide 87 (Method-of-Moments) || sigma - standard deviation || See Lecture 3, Slide 85, Method-of-Moments for an example || beta - shape ||  || k - shape ||  ||
 * **Family** || **Parameters** || **How To Compute** ||
 * Bernoulli || p = probability || Need to figure the probability ||
 * Binomial || n = number
 * [|Hyper geometric] || n = total drawn
 * Poisson || lambda || lambda is E[X], hopefully can compute if not given. ||
 * Geometric || p = probability || Probablly given; just use ||
 * [|Uniform] (Continuous) || a = starting x value
 * Exponential || lambda || 1/lambda is E[X], hopefully can compute if not given. ||
 * [|Gamma] || alpha = ~shape~
 * Normal || mu - mean
 * [|Beta] || alpha - shape
 * [|Weibull] || lamba - shape


 * Sources:**
 * EMSE 271, Fall 2009
 * Wikipedia