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Topic: Conditional distribution

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Probability distribution

Conditional probability

	Conditional probability - Wikipedia, the free encyclopedia
		Conditional probability is the probability of some event A, given the occurrence of some other event B.
		In other words, if A and B are independent, then the conditional probability of A, given B is simply the individual probability of A alone; likewise, the probability of B given A is simply the probability of B alone.
		Conditional probability can be calculated with a decision tree.
	en.wikipedia.org /wiki/Conditional_probability (593 words)

	Forecast Verification Glossary
		A 2-dimensional histogram is a diagram plotting the marginal distribution of a variable in terms of its frequency of occurrence.
		conditional distribution of the observations, given the forecast probability, is plotted against the forecast probability.
		conditional, or marginal distribution of forecasts and observations or of the correspondence between forecasts and observations.
	www.sec.noaa.gov /forecast_verification/Glossary.html (3068 words)

	Bayes' theorem - Wikipedia, the free encyclopedia
		The probability of an event A conditional on another event B is generally different from the probability of B conditional on A.
		It is often helpful when calculating conditional probabilities to create a simple table containing the number of occurrences of each outcome, or the relative frequencies of each outcome, for each of the independent variables.
		The conditional distribution of A given the "data" B is the posterior probability distribution or just the posterior.
	en.wikipedia.org /wiki/Bayes%27_theorem (2561 words)

	Conditional distribution - Wikipedia, the free encyclopedia
		Given two jointly distributed random variables X and Y, the conditional probability distribution of Y given X (written "Y
		X") is the probability distribution of Y when X is known to be a particular value.
		The concept of the conditional distribution of a continuous random variable is not as intuitive as it might seem: Borel's paradox shows that conditional probability density functions need not be invariant under coordinate transformations.
	en.wikipedia.org /wiki/Conditional_distribution (266 words)

	Nonparametric Tests for Conditional Independence
		Liangjun Su As David (1979) puts it, independence and conditional independence form the basis of probability theory and are equally fundamental in the theory of statistical inference.
		In a seminal paper, Granger (1980) introduces the concept of Granger non-causality at the distribution level, which is a particular case of conditional independence.
		Further, conditional independence is often used as an identification condition in econometrics (e.g., Imbens and Newey, 2001; Lechner and Miquel, 2002).
	econ.ucsd.edu /~lsu/abstractshtml.html (712 words)

	[No title]
		Using this new joint probability distribution, the conditional distribution of the labels given the input variables can be found by dividing the joint probability by the marginal distribution of the joint distribution summed over the possible labels.
		The conditional distribution is defined by equations 1.16 and 1.17.
		By considering the conditional distribution p(yx) that follows from the joint distribution p(y, x) of an HMM, we can find that this conditional distribution is a conditional random field with a particular choice of feature functions.
	www.cs.wisc.edu /~apirak/cs/cs838/reviews_score_19.html (4828 words)

	AMS 311
		The conditional mean is the mean of the conditional distribution, and the conditional variance is the variance of the conditional distribution.
		be a sequence of independent and identically distributed random variables and let N be a nonnegative integer-values random variable that is independent of the sequence
		The conditional variance formula is the key result needed to prove a result in mathematical statistics known as the Rao-Blackwell theorem.
	www.ams.sunysb.edu /~dorothy/handout30.html (438 words)

	Holism and Nonseparability in Physics
		Quantum mechanics predicts the probability distributions for combinations of joint and single measurements of variables including spin and polarization on each of a pair of entangled systems, and many of these distributions have been experimentally verified.
		In that case, it would rather be the probability distribution conditional on a complete specification of the values of the hidden variables that should be taken to constitute irreducible dispositions of the system concerned.
		Outcome independence may be contrasted with parameter independence — the condition that, given a definite hidden variable assignment, the outcome of a measurement on one of a pair of entangled systems is probabilistically independent of what measurement, if any, is made on the other system.
	plato.stanford.edu /entries/physics-holism (8207 words)

	Approximating conditional distribution functions using dimension reduction, Peter Hall, Qiwei Yao
		Motivated by applications to prediction and forecasting, we suggest methods for approximating the conditional distribution function of a random variable Y given a dependent random d-vector X.
		Bhattacharya, P. and Gangopadhyay, A. Kernel and nearest-neighbor estimation of a conditional quantile.
		Hall, P. and Yao, Q. Estimating conditional distribution functions using dimension reduction.
	projecteuclid.org /getRecord?id=euclid.aos/1120224107 (659 words)

	SSRN-Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators? by Hannes Leeb, Benedikt ...
		We consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model.
		The notion of a post-model-selection estimator here refers to the combined procedure resulting from first selecting a model (e.g., by a model selection criterion like AIC or by a hypothesis testing procedure) and second estimating the parameters in the selected model (e.g., by least-squares or maximum likelihood), all based on the same data set.
		Similar impossibility results are also obtained for the conditional distribution of linear functions (e.g., predictors) of the post-model-selection estimator.
	papers.ssrn.com /sol3/papers.cfm?abstract_id=460984 (311 words)

	Statistics:Numerical Methods/Quantile Regression - Wikibooks, collection of open-content textbooks
		This leads to an approximation of the mean function of the conditional distribution of the dependent variable.
		For instance, on the outer bounds, where the blue is very light, the probability density for the given data set is relatively low, as they are marked by the quantiles 0,05 to 0,1 and 0,9 to 0,95.
		Frequently, error terms are not constant across a distribution, thereby violating the axiom of homoscedasticity.
	en.wikibooks.org /wiki/Statistics:Numerical_Methods/Quantile_Regression (3130 words)

	No Title
		Since expected values of random variables may be computed from distribution functions, we may define the conditional expected value of X given Y = y to be the expected value of X as computed with F
		showing that conditional expectation is an extension of the idea of conditional probability.
		It is quite typical to be given not the joint distribution of X and Y, but instead the distribution of Y and the conditional distribution of X given Y = y.
	www.uwm.edu /~ericskey/361material/361F98/L21/index.html (358 words)

	Math 413
		Calculate the conditional probability distribution of X given that Y
		(a) Calculate the conditional probability function of X given that X+Y= 6.
		(b) Is the result in (a) the same as the hypergeometric distribution with parameters 8 (= 2n) and
	www.cwu.edu /~chueh/math413_1.htm (159 words)

	Conditional Distribution and Conditional Expectation
		The conditional probability mass function of Y given X is:
		The conditional expectation of a random variable Y is the expected value of Y given [X=x], and is denoted: E[Y
		The conditional expectation of Y given X=i is:
	www.utdallas.edu /~jjue/cs6352/probability/node3.html (145 words)

	[No title]
		A variety of methods are investigated, from conditional distributions and multivariate polynomial regressions (using Chebychev polynomials) to hybridized neural networks.
		Mathematical moments (moments and holding periods, moments and distributions, moments and day of the week, moments and seasonality, moments and expiration date).
		Conditional distributions (historical volatility: conditional distributions vs. Black-Scholes; regression-estimated volatility: conditional distributions vs. Black-Scholes; detrended distributions: conditional distributions vs. Black-Scholes; distributions and the volatility payoff; skew and kurtosis as variables in a conditional distribution; conditional distributions and venue; technical indicators as conditioning variables).
	www.scientific-consultants.com /optionsbook.html (840 words)

	1 Introduction
		Therefore, the least squares estimate (or a more robust alternative) of the conditional expectation and some associated measure of dispersion would usually be a satisfactory result in so simple model.
		However, it is imaginable that the effects of price or advertisement on the amount of sales are quite different for a commodity sold in high volumes and a similar one with low sales.
		Hence, similarly to the heteroscedasticity case, we see that the conditional quantile functions are not necessarily just vertically shifted with respect to each other, and consequently, their estimation can provide a more complete description of the model under consideration than a usual expectation-oriented regression.
	sunsite.univie.ac.at /XploRe/tutorials/qrnode2.html (648 words)

	Higher-order approximations to conditional distribution functions, John E. Kolassa
		This paper derives higher-order terms in the double-saddlepoint expansion of Skovgaard for a unidimensional conditional cumulative distribution function.
		KOLASSA, J. and TANNER, M. Approximate conditional inference in exponential families via the Gibbs sampler.
		TEMME, N. The uniform asy mptotic expansion of a class of integrals related to cumulative distribution functions.
	projecteuclid.org /getRecord?id=euclid.aos/1033066213 (198 words)

	Math Forum Discussions
		1.(Y) first we have one "procedure" that is normal distributed with, expected value, my=0 and, variance, o^2=2.
		that means that (2) is considered to be a conditional 'distribution'.
		The Math Forum is a research and educational enterprise of the Drexel School of Education.
	mathforum.org /kb/thread.jspa?threadID=1253598&messageID=3993468 (156 words)

	EconPapers: Modeling movie success when "nobody knows anything": Conditional stable distribution analysis of film ...
		The conditional distribution of box-office returns is analyzed using the stable distribution regression model.
		The regression coefficients in this model represent what is known about the correlates of film success while at the same time permitting the variance of film success at the box office to be infinite.
		The empirical analysis shows that the conditional distribution of film returns has infinite variance, and this invalidates statistical inferences from the often-applied least-squares regression model.
	econpapers.repec.org /paper/ecmfeam04/409.htm (293 words)

	Are There Asymmetries in the Effects of Training on the Conditional Male Wage Distribution?
		We use a quantile regression framework to investigate the degree to which work-related training affects the location, scale and shape of the conditional wage distribution.
		In other words, the way in which unobservables interact with training is fairly uniform across the conditional distribution.
		In addition, the returns at the upper parts of the conditional distribution are much higher than at the lower parts of the distribution.
	ideas.repec.org /p/ese/iserwp/2004-01.html (1086 words)

	Cool Solutions: Conditional Application Distribution using Fault Tolerance
		Consequently, it would be best to mimic NAL's normal behavior, where only a single application icon gets delivered to the user and - once the application is launched - NAL makes sure that all necessary components are installed.
		Hard to believe, but there is indeed a very simple solution for delivering both the application and a conditional setup though a single NAL icon even without snAppshot.
		The key to the solution is to use a much-neglected feature in NAL -- Fault Tolerance.
	www.novell.com /coolsolutions/trench/2893.html (886 words)

	[No title]
		FULL COLUMN RANK of the matrix X. Expected value of deviations from the conditional mean.
		If so, then we have not fully specified the conditional mean, and this function we are calling ‘E[yx]’ is not the conditional mean (regression).
		Simplifies some proofs, but is without content in the context of the theory and is essentially irrelevant to the results we will obtain.
	www.bus.ucf.edu /kim/eco7426/Greene-Notes02.doc (345 words)

	A Conditional Distribution Model (ResearchIndex)
		We argue that neither a censored nor a truncated distribution model is appropriate for the aggregate return.
		The proposed mixed beta distribution allows for varying conditional mean and volatility, and with increasing volatility it changes from leptokurtic to platykurtic densities.
		Stochastics for the Worst Case : Distributions and Risk - Measures For Minimal
	citeseer.ist.psu.edu /682759.html (211 words)

	Citebase - Approximating conditional distribution functions using dimension reduction (Site not responding. Last check: 2007-10-31)
		Citebase - Approximating conditional distribution functions using dimension reduction
		Motivated by applications to prediction and forecasting, we suggest methods for approximating the conditional distribution function of a random variable Y given a dependent random d-vector X. The idea is to estimate not the distribution of YX, but that of Yθ
		X, has the same first-order asymptotic properties that it would enjoy if θ were known.
	citebase.eprints.org /cgi-bin/citations?id=oai:arXiv.org:math/0507432 (209 words)

	Citebase - A Sharp Inequality for Conditional Distribution of the First Exit Time of Brownian Motion (Site not responding. Last check: 2007-10-31)
		Citebase - A Sharp Inequality for Conditional Distribution of the First Exit Time of Brownian Motion
		A Sharp Inequality for Conditional Distribution of the First Exit Time of Brownian Motion
		Let U be a domain, convex in x and symmetric about the y-axis, which is contained in a centered and oriented rectangle R.
	citebase.eprints.org /cgi-bin/citations?id=oai:arXiv.org:math/0502057 (128 words)

	Fitting a Conditional Gaussian Distribution - Murphy (ResearchIndex) (Site not responding. Last check: 2007-10-31)
		Give feedback on RSS feeds for document recommendations in CiteSeer.
		0.1: MAP Estimation of Unobserved Variables in Conditional Gaussian..
		@misc{ murphy98fitting, author = "K. Murphy", title = "Fitting a conditional gaussian distribution", text = "K. Murphy.
	citeseer.ist.psu.edu /386015.html (431 words)

	Energy Citations Database (ECD) - Energy and Energy-Related Bibliographic Citations
		Energy Citations Database (ECD) Document #4334957 - USE OF CONDITIONAL DISTRIBUTION FUNCTION IN THE THEORY OF GAS PLASMA.
		Availability information may be found in the Availability, Publisher, Research Organization, Resource Relation and/or Author (affiliation information) fields and/or via the "Full-text Availability" link.
		USE OF CONDITIONAL DISTRIBUTION FUNCTION IN THE THEORY OF GAS PLASMA.
	www.osti.gov /energycitations/product.biblio.jsp?osti_id=4334957 (116 words)

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