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# Topic: Probability distribution

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 Probability distribution The Probability distribution of the sum of two random variables is the convolution of each of their distributions. The Probability distribution of the difference of two random variables is the cross-correlation of each of their distributions. The triangular distribution on [''a'', b], a special case of which is the distribution of the sum of two uniformly distributed random variables (the convolution of two uniform distributions). probability-distribution.mindbit.com   (0 words)

 VBA11 - Multivariate Standard Normal Probability Distribution The procedure for generating random numbers from a multivariate distribution is described in the 4 steps of the example shown later. The probability computed in this program is based on the area under the probability distribution from negative infinity to z. For example, the standard deviation and the mean from a standard normal probability distribution should be 1 and 0, respectively. www.anthony-vba.kefra.com /vba/vba11.htm   (317 words)

 Probability distribution - Wikipedia, the free encyclopedia A probability distribution is a special case of the more general notion of a probability measure, which is a function that assigns probabilities satisfying the Kolmogorov axioms to the measurable sets of a measurable space. The probability distribution of the sum of two independent random variables is the convolution of each of their distributions. The rectangular distribution is a uniform distribution on [-1/2,1/2]. en.wikipedia.org /wiki/Probability_distribution   (1449 words)

 Probability - Wikipedia, the free encyclopedia Probabilities are equivalently expressed as odds, which is the ratio of the probability of one event to the probability of all other events. Probability applications include even more than statistics, which is usually based on the idea of probability distributions and the central limit theorem. Governments typically apply probability methods in environmental regulation where it is called "pathway analysis", and are often measuring well-being using methods that are stochastic in nature, and choosing projects to undertake based on statistical analyses of their probable effect on the population as a whole. en.wikipedia.org /wiki/Probability   (2822 words)

 Lecture Notes 6 Among the continuous probability distribution, the uniform distribution is the simplest one of all. The probability associated with Z = -1.36 is 0.4131, and the probability associated with Z = -0.34 is 0.1331. From the table, the probability for this Z score is 0.3023 which is the probability between the mean (56) and 60. business.clayton.edu /arjomand/business/l6.html   (0 words)

 PlanetMath: probability distribution function The main feature of a probability distribution function is that it induces a probability measure This is version 6 of probability distribution function, born on 2002-04-29, modified 2006-12-12. Object id is 2884, canonical name is Distribution. planetmath.org /encyclopedia/Distribution2.html   (0 words)

 CPC - Introduction to Probability of Exceedance The middle 86% of the probability distribution, ranging from the 93% to the 7% probability of exceedance values, is considered to be reasonably well sampled, in contrast with the outer 7% tails. The lower amount corresponds to the 25%ile (75% probability of exceedance) of the forecast distribution, and the upper amount is the amount that corresponds to the 75%ile (25% probability of exceedance) of the forecast distribution. The probabilities of exceedance are expressions of uncertainty inherent in the climatological distribution and in the final forecast which is conditional on the current and expected climate state. www.cpc.ncep.noaa.gov /pacdir/NFORdir/INTR.html   (0 words)

 Discrete Probability Distribution - NEGATIVE BINOMIAL DISTRIBUTION Thus, the product of these three terms is the probability that there are r successes and x-r failures in the x trials, with the rth success occurring on the xth trial. A random variable X, having a negative binomial distribution with parameters r and p, is the sum of r independent random variables, each one geometrically distributed with parameter p. In fact, a geometric distribution with parameter p is the same as a negative binomial distribution with parameters n = 1 and p. library.thinkquest.org /10030/6dpdnbd.htm   (0 words)

 math lessons - Probability distribution The Ewens sampling formula is a probability distribution on the set of all partitions of an integer n, arising in population genetics. The rectangular distribution is a uniform distribution on [-1/2,1/2]. It represents a discrete probability distribution concentrated at 0 — a degenerate distribution — but the notation treats it as if it were a continuous distribution. www.mathdaily.com /lessons/Probability_distribution   (1254 words)

 Probability, Distributions Without probability theory, there would be no way to describe the way samples might differ from the populations from which they were drawn. The probability that something is true for an individual selected at random from a population is equal to the fraction of the population for whom it is true. For example, the distribution of the difference between two sample means describes what is likely to happen when two samples are drawn and the difference in their means is calculated. www.tufts.edu /~gdallal/prob.htm   (884 words)

 Probability distribution Info - Encyclopedia WikiWhat.com   (Site not responding. Last check: ) A probability distribution assigns to every interval of the real numbers a probability, so that the probability axioms are satisfied. The Boltzmann distribution, a discrete distribution important in statistical physics which describes the probabilities of the various energy levels of a system in thermal equilibrium. In physics it is usually called a Lorentzian, and it is the distribution of the energy of an unstable state in quantum mechanics. www.wikiwhat.com /encyclopedia/p/pr/probability_distribution.html   (913 words)

 Apply probability models to Web data using PHP When developing a probability model for a random variable, it is often more useful to express the expected frequency of different outcomes in terms of probabilities that vary between 0 and 1 instead of using raw frequency counts. When you look at the observed probability distribution for male height, it appears to have a symmetrical bell shape that is reminiscent of the plot for a normally distributed random variable. When the observed distribution of your random variable is exponentially distributed, understanding the likelihood of different outcomes is much harder to do by intuition, so it helps in these cases especially to have a formal probability model for the random variable. www-128.ibm.com /developerworks/web/library/wa-probab   (7538 words)

 Apply probability models to Web data using PHP When developing a probability model for a random variable, it is often more useful to express the expected frequency of different outcomes in terms of probabilities that vary between 0 and 1 instead of using raw frequency counts. When you look at the observed probability distribution for male height, it appears to have a symmetrical bell shape that is reminiscent of the plot for a normally distributed random variable. To assess how good the fit is between the observed probability distribution and a normal distribution, you could generate expected frequencies for each height interval based on what would be expected from a normally distributed random variable with a mean of 70.31 and a standard deviation 2.61. www-106.ibm.com /developerworks/web/library/wa-probab   (7538 words)

 Random Variables - PROBABILITY DISTRIBUTIONS - JOINT PROBABILITY DISTRIBUTION The joint probability distribution of two discrete random variables X and Y is a function whose domain is the set of ordered pairs (x, y), where x and y are possible values for X and Y, respectively, and whose range is the set of probability values corresponding to the ordered pairs in its domain. The joint probability function of two discrete random variables is related functionally to the probability mass function of either random variable. The probability mass function of a random variable can be derived from its joint probability distribution with another random variable (or a set of random variables) by summing the joint probability distribution across all possible values of the other random variable(s). library.thinkquest.org /10030/5rvpdjpd.htm   (0 words)

 Probability The sum of the probabilities of all of the outcomes is 1.0 Any set of the outcomes has a probability, which is the sum of the probabilities of the members of the subset. These numbers represent their probabilities where the universe in question is a universe of single words being chosen from the universe of possibilities -- their probabilities in a unigram word model. The probability of a sentence S in the unigram model is the product of the probabilities of its words, so the log probability of a sentence in the unigram model is the sum of the log probabilities of its words. humanities.uchicago.edu /faculty/goldsmith/Industrial/Probability.htm   (0 words)

 Moments of a Probability Distribution We might refer to the function as a distribution, whether or not we consider it to be a probability density distribution. This follows from the facts that probability distributions are normalized so that the area under the curve is always 1, (hence the zero'th moment is 1) and the average, or mean of the distribution is defined by the integral that also happens to be the first moment. Consider the continuous distribution, in which the probability is constant for values of x in the interval [a,b] and zero for values outside this interval. www.ugrad.math.ubc.ca /coursedoc/math101/notes/moreApps/moments.html   (0 words)

 Statistics Glossary - Probability Probability is conventionally expressed on a scale from 0 to 1; a rare event has a probability close to 0, a very common event has a probability close to 1. The probability density function of a continuous random variable is a function which can be integrated to obtain the probability that the random variable takes a value in a given interval. The Geometric distribution is related to the Binomial distribution in that both are based on independent trials in which the probability of success is constant and equal to p. www.cas.lancs.ac.uk /glossary_v1.1/prob.html   (0 words)

 Statistics 2 - Normal Probability Distribution The inverse normal probability distribution function will find the precise value at a given percent based upon the mean and standard deviation. Given a normal distribution of values for which the mean is 70 and the standard deviation is 4.5. Graph and investigate the normal distribution curve where the mean is 0 and the standard deviation is 1. mathbits.com /MathBits/TISection/Statistics2/normaldistribution.htm   (0 words)

 Apply probability models to Web data using PHP When developing a probability model for a random variable, it is often more useful to express the expected frequency of different outcomes in terms of probabilities that vary between 0 and 1 instead of using raw frequency counts. When you look at the observed probability distribution for male height, it appears to have a symmetrical bell shape that is reminiscent of the plot for a normally distributed random variable. When the observed distribution of your random variable is exponentially distributed, understanding the likelihood of different outcomes is much harder to do by intuition, so it helps in these cases especially to have a formal probability model for the random variable. www.ibm.com /developerworks/web/library/wa-probab   (0 words)

 The Hydrogen Atom - The Probability Distribution of the Hydrogen Atom The wave function and probability distribution as functions of r for the n = 1 level of the H atom. A "picture" of the electron density distribution for the electron in the n = 1 level of the hydrogen atom would be a spherical ball of charge, dense around the nucleus and becoming increasingly diffuse as the value of r is increased. The angular dependence of the atomic orbitals for the hydrogen atom and the shapes of the contours of the corresponding electron density distributions are intimately connected with the angular momentum possessed by the electron. www.chemistry.mcmaster.ca /esam/Chapter_3/section_2.html   (3136 words)

 Distribution Form Frame   (Site not responding. Last check: ) Use the binomial probability when the outcome of experiments is success or failure, when the experiments are independent of one another, and when the probability of success doesn't change in successive trials. The distribution is used in the analysis of variance and is a function of the ratio of two independent random variables each of which has a chi-square distribution and is divided by its number of degrees of freedom. The Gumbel distribution, a special case of the Fisher-Tippett Distribution, is particularly convenient for extreme value distribution purposes, and it may be used as an alternative to the normal distribution in the case of skewed empirical data. ic.net /~jnbohr/java/CdfDemoArgs.html   (1657 words)

 NLTK Tutorial: Probability   (Site not responding. Last check: ) Probability distributions are often used to estimate the likelihood of each sample outcome for an experiment. Although MLE is the simplest probability distribution, it has a tendency to over-estimate the probabilities of sample outcomes that only occur a few times (and to under-estimate the probabilities of samples that never occur). Conditional probability distributions are useful for examining the effect that a condition has on an experiment's probability distribution. nltk.sourceforge.net /tutorial/probability/nochunks.html   (2089 words)

 Probability 101   (Site not responding. Last check: ) A probability measure is a function that defines the probability of all different events. There are no probability density functions for discrete variables because their probability distribution functions are also discrete and cannot be derived. In probability, the relative frequency of certain events is known for a population and we ask for the probability of events for a subset of this population. www.cs.cornell.edu /Courses/CS664/1997sp/probability.htm   (1188 words)

 Probability Distribution The probability distribution of a discrete random variable is a list of probabilities associated with each of its possible values. The connection between the moments of a probability distribution and its characteristic function is seen from taking the derivaative of the characteristic function with respect to the parameter ω. The effect of diffraction on the probability distribution of a quantum particle can be studied by considering a wave packet that passes through a narrow slit. www.lycos.com /info/probability-distribution.html   (321 words)

 probability equations A conditional probability is the probability of an event given that another event has occurred. The probability of a total greater than 8 given that the first die is 6 is therefore 4/6 = 2/3. ,,A] =3/51=1/17 and the probability of A and B is: 1/13×1/17=1/221. cnx.org /content/m10244/latest   (0 words)

 1.3.6. Probability Distributions Probability distributions are a fundamental concept in statistics. In this case, the distribution does not need to be the best-fitting distribution for the data, but an adequate enough model so that the statistical technique yields valid conclusions. Simulation studies with random numbers generated from using a specific probability distribution are often needed. www.itl.nist.gov /div898/handbook/eda/section3/eda36.htm   (0 words)

 NSCAT Probability Distribution Study In wind retrieval, the NSCAT sigma-0 measurements are assumed to have a Gaussian probability distribution with a variance which depends on the mean. Given this distribution model, the maximum-likelihood estimator is formed and optimized to estimate the wind. The observed NSCAT probability distribution (pdf) will be the combination (convolution) of the pdf described above and the pdf of the sigma-0 of the surface. www.scp.byu.edu /gallery/NSCAT_pdf.html   (764 words)

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