Probability distribution

Jump to: navigation, search

WikiDoc Resources for Probability distribution

Articles

Most recent articles on Probability distribution

Most cited articles on Probability distribution

Review articles on Probability distribution

Articles on Probability distribution in N Eng J Med, Lancet, BMJ

Media

Powerpoint slides on Probability distribution

Images of Probability distribution

Photos of Probability distribution

Podcasts & MP3s on Probability distribution

Videos on Probability distribution

Evidence Based Medicine

Cochrane Collaboration on Probability distribution

Bandolier on Probability distribution

TRIP on Probability distribution

Clinical Trials

Ongoing Trials on Probability distribution at Clinical Trials.gov

Trial results on Probability distribution

Clinical Trials on Probability distribution at Google

Guidelines / Policies / Govt

US National Guidelines Clearinghouse on Probability distribution

NICE Guidance on Probability distribution

NHS PRODIGY Guidance

FDA on Probability distribution

CDC on Probability distribution

Books

Books on Probability distribution

News

Probability distribution in the news

Be alerted to news on Probability distribution

News trends on Probability distribution

Commentary

Blogs on Probability distribution

Definitions

Definitions of Probability distribution

Patient Resources / Community

Patient resources on Probability distribution

Discussion groups on Probability distribution

Patient Handouts on Probability distribution

Directions to Hospitals Treating Probability distribution

Risk calculators and risk factors for Probability distribution

Healthcare Provider Resources

Symptoms of Probability distribution

Causes & Risk Factors for Probability distribution

Diagnostic studies for Probability distribution

Treatment of Probability distribution

Continuing Medical Education (CME)

CME Programs on Probability distribution

International

Probability distribution en Espanol

Probability distribution en Francais

Business

Probability distribution in the Marketplace

Patents on Probability distribution

Experimental / Informatics

List of terms related to Probability distribution

Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]

Associate Editor-In-Chief: Cafer Zorkun, M.D., Ph.D. [2]

Please Take Over This Page and Apply to be Editor-In-Chief for this topic: There can be one or more than one Editor-In-Chief. You may also apply to be an Associate Editor-In-Chief of one of the subtopics below. Please mail us [3] to indicate your interest in serving either as an Editor-In-Chief of the entire topic or as an Associate Editor-In-Chief for a subtopic. Please be sure to attach your CV and or biographical sketch.

Overview

A probability distribution describes the values and probabilities that a random event can take place. The values must cover all of the possible outcomes of the event, while the total probabilities must sum to exactly 1, or 100%. For example, a single coin flip can take values Heads or Tails with a probability of exactly 1/2 for each; these two values and two probabilities make up the probability distribution of the single coin flipping event. This distribution is called a discrete distribution because there are a countable number of discrete outcomes with positive probabilities.

A continuous distribution describes events over a continuous range, where the probability of a specific outcome is zero. For example, a dart thrown at a dartboard has essentially zero probability of landing at a specific point, since a point is vanishingly small, but it has some probability of landing within a given area. The probability of landing within the small area of the bullseye would (hopefully) be greater than landing on an equivalent area elsewhere on the board. A smooth function that describes the probability of landing anywhere on the dartboard is the probability distribution of the dart throwing event. The integral of the probability density function (pdf) over the entire area of the dartboard (and, perhaps, the wall surrounding it) must be equal to 1, since each dart must land somewhere.

The concept of the probability distribution and the random variables which they describe underlies the mathematical discipline of probability theory, and the science of statistics. There is spread or variability in almost any value that can be measured in a population (e.g. height of people, durability of a metal, etc.); almost all measurements are made with some intrinsic error; in physics many processes are described probabilistically, from the kinetic properties of gases to the quantum mechanical description of fundamental particles. For these and many other reasons, simple numbers are often inadequate for describing a quantity, while probability distributions are often more appropriate models. There are, however, considerable mathematical complications in manipulating probability distributions, since most standard arithmetic and algebraic manipulations cannot be applied.

Rigorous definitions

In probability theory, every random variable may be attributed to a function defined on a state space equipped with a probability distribution that assigns a probability to every subset (more precisely every measurable subset) of its state space in such a way that the probability axioms are satisfied. That is, probability distributions are probability measures defined over a state space instead of the sample space. A random variable then defines a probability measure on the sample space by assigning a subset of the sample space the probability of its inverse image in the state space. In other words the probability distribution of a random variable is the push forward measure of the probability distribution on the state space.

Probability distributions of real-valued random variables

Because a probability distribution Pr on the real line is determined by the probability of being in a half-open interval Pr(ab], the probability distribution of a real-valued random variable X is completely characterized by its cumulative distribution function:

Discrete probability distribution

A probability distribution is called discrete if its cumulative distribution function only increases in jumps.

The set of all values that a discrete random variable can assume with non-zero probability is either finite or countably infinite because the sum of uncountably many positive real numbers (which is the smallest upper bound of the set of all finite partial sums) always diverges to infinity. Typically, the set of possible values is topologically discrete in the sense that all its points are isolated points. But, there are discrete random variables for which this countable set is dense on the real line.

Discrete distributions are characterized by a probability mass function, such that

Continuous probability distribution

By one convention, a probability distribution is called continuous if its cumulative distribution function is continuous, which means that it belongs to a random variable X for which Pr[ X = x ] = 0 for all x in R.

Another convention reserves the term continuous probability distribution for absolutely continuous distributions. These distributions can be characterized by a probability density function: a non-negative Lebesgue integrable function defined on the real numbers such that

Discrete distributions and some continuous distributions (like the devil's staircase) do not admit such a density.

Terminology

The support of a distribution is the smallest closed set whose complement has probability zero.

The probability density function of the sum of two independent random variables is the convolution of each of their density functions.

The probability density function of the difference of two random variables is the cross-correlation of each of their density functions.

A discrete random variable is a random variable whose probability distribution is discrete. Similarly, a continuous random variable is a random variable whose probability distribution is continuous.

List of important probability distributions

Certain random variables occur very often in probability theory, in some cases due to their application to many natural and physical processes, and in some cases due to theoretical reasons such as the central limit theorem, the Poisson limit theorem, or properties such as memorylessness or other characterizations. Their distributions therefore have gained special importance in probability theory.

Discrete distributions

With finite support

  • The Bernoulli distribution, which takes value 1 with probability p and value 0 with probability q = 1 − p.
  • The Rademacher distribution, which takes value 1 with probability 1/2 and value −1 with probability 1/2.
  • The binomial distribution describes the number of successes in a series of independent Yes/No experiments.
  • The degenerate distribution at x0, where X is certain to take the value x0. This does not look random, but it satisfies the definition of random variable. It is useful because it puts deterministic variables and random variables in the same formalism.
  • The discrete uniform distribution, where all elements of a finite set are equally likely. This is supposed to be the distribution of a balanced coin, an unbiased die, a casino roulette or a well-shuffled deck of playing cards. Also, one can use measurements of quantum states to generate uniform random variables. All these are "physical" or "mechanical" devices, subject to design flaws or perturbations, so the uniform distribution is only an approximation of their behaviour. In digital computers, pseudo-random number generators are used to produce a statistically random discrete uniform distribution.
  • The hypergeometric distribution, which describes the number of successes in the first m of a series of n Yes/No experiments, if the total number of successes is known.
  • Zipf's law or the Zipf distribution. A discrete power-law distribution, the most famous example of which is the description of the frequency of words in the English language.
  • The Zipf-Mandelbrot law is a discrete power law distribution which is a generalization of the Zipf distribution.

With infinite support

Continuous distributions

Supported on a bounded interval

  • The Beta distribution on [0,1], of which the uniform distribution is a special case, and which is useful in estimating success probabilities.

Supported on semi-infinite intervals, usually [0,∞)

Supported on the whole real line

Joint distributions

For any set of independent random variables the probability density function of their joint distribution is the product of their individual density functions.

Two or more random variables on the same sample space

Matrix-valued distributions

Miscellaneous distributions

See also

External links




Cost Effectiveness of Probability distribution

| group5 = Clinical Trials Involving Probability distribution | list5 = Ongoing Trials on Probability distribution at Clinical Trials.govTrial results on Probability distributionClinical Trials on Probability distribution at Google


| group6 = Guidelines / Policies / Government Resources (FDA/CDC) Regarding Probability distribution | list6 = US National Guidelines Clearinghouse on Probability distributionNICE Guidance on Probability distributionNHS PRODIGY GuidanceFDA on Probability distributionCDC on Probability distribution


| group7 = Textbook Information on Probability distribution | list7 = Books and Textbook Information on Probability distribution


| group8 = Pharmacology Resources on Probability distribution | list8 = AND (Dose)}} Dosing of Probability distributionAND (drug interactions)}} Drug interactions with Probability distributionAND (side effects)}} Side effects of Probability distributionAND (Allergy)}} Allergic reactions to Probability distributionAND (overdose)}} Overdose information on Probability distributionAND (carcinogenicity)}} Carcinogenicity information on Probability distributionAND (pregnancy)}} Probability distribution in pregnancyAND (pharmacokinetics)}} Pharmacokinetics of Probability distribution


| group9 = Genetics, Pharmacogenomics, and Proteinomics of Probability distribution | list9 = AND (pharmacogenomics)}} Genetics of Probability distributionAND (pharmacogenomics)}} Pharmacogenomics of Probability distributionAND (proteomics)}} Proteomics of Probability distribution


| group10 = Newstories on Probability distribution | list10 = Probability distribution in the newsBe alerted to news on Probability distributionNews trends on Probability distribution


| group11 = Commentary on Probability distribution | list11 = Blogs on Probability distribution

| group12 = Patient Resources on Probability distribution | list12 = Patient resources on Probability distributionDiscussion groups on Probability distributionPatient Handouts on Probability distributionDirections to Hospitals Treating Probability distributionRisk calculators and risk factors for Probability distribution


| group13 = Healthcare Provider Resources on Probability distribution | list13 = Symptoms of Probability distributionCauses & Risk Factors for Probability distributionDiagnostic studies for Probability distributionTreatment of Probability distribution

| group14 = Continuing Medical Education (CME) Programs on Probability distribution | list14 = CME Programs on Probability distribution

| group15 = International Resources on Probability distribution | list15 = Probability distribution en EspanolProbability distribution en Francais

| group16 = Business Resources on Probability distribution | list16 = Probability distribution in the MarketplacePatents on Probability distribution

| group17 = Informatics Resources on Probability distribution | list17 = List of terms related to Probability distribution


}} ar:توزيع احتمالي cs:Rozdělení pravděpodobnosti de:Wahrscheinlichkeitsverteilung eo:Probabla distribuo fa:توزیع احتمال gl:Distribución de probabilidade it:Distribuzione di probabilità he:התפלגות lt:Skirstinys nl:Kansverdeling su:Sebaran probabilitas sv:Sannolikhetsfördelning



Linked-in.jpg