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Power series derived from a discrete probability distribution
In probability theory, the probability generating function of a discrete random variable is a power series representation (the generating function) of
Probability generating function
Probability_generating_function
Concept in probability theory and statistics
In probability theory and statistics, the moment generating function of a real-valued random variable is a generating function that provides an alternative
Moment_generating_function
Fourier transform of the probability density function
include the moment-generating function and the probability-generating function. The characteristic function exists for all probability distributions. This
Characteristic function (probability theory)
Characteristic_function_(probability_theory)
Description of continuous random distribution
In probability theory, a probability density function (PDF), density function, or simply density of an absolutely continuous random variable, is a function
Probability_density_function
Discrete-variable probability distribution
In probability and statistics, a probability mass function (sometimes called probability function or frequency function) is a function that gives the
Probability_mass_function
Formal power series
is the probability mass function of a discrete random variable, then its ordinary generating function is called a probability-generating function. The exponential
Generating_function
Probability that random variable X is less than or equal to x
In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X {\displaystyle X} , or just distribution
Cumulative distribution function
Cumulative_distribution_function
Set of quantities in probability theory
the cumulant generating function (CGF) K(t), which is a generating function that is the natural logarithm of the moment generating function: K ( t ) = log
Cumulant
Statistical function that defines the quantiles of a probability distribution
In probability and statistics, a probability distribution's quantile function is the inverse of its cumulative distribution function. That is, the quantile
Quantile_function
Mathematical function for the probability a given outcome occurs in an experiment
probability function, the cumulative distribution function, the probability mass function and the probability density function, the moment generating
Probability_distribution
French polymath (1749–1827)
to a different variable. The latter is therefore called the probability-generating function of the former. Laplace then shows how, by means of interpolation
Pierre-Simon_Laplace
Generalization of the concept from statistical mechanics
The partition function or configuration integral, as used in probability theory, information theory and dynamical systems, is a generalization of the definition
Partition function (mathematics)
Partition_function_(mathematics)
Discrete probability distribution
} One derivation of this uses probability-generating functions. Consider a Bernoulli trial (coin-flip) whose probability of one success (or expected number
Poisson_distribution
In probability theory and statistics, the factorial moment generating function (FMGF) of the probability distribution of a real-valued random variable
Factorial moment generating function
Factorial_moment_generating_function
Compound Poisson-family discrete probability distribution
develops, we must bear in mind that the probability mass function is calculated from the probability generating function, and use the property of Stirling Numbers
Neyman_Type_A_distribution
Probability distribution
gamma function. Using that f ( . ; m, r, ps) for s ∈ (0, 1] is also a probability mass function, it follows that the probability generating function is given
Extended negative binomial distribution
Extended_negative_binomial_distribution
Moment of a random variable minus its mean
expectation operator. For a continuous univariate probability distribution with probability density function f(x), the n-th moment about the mean μ is μ n
Central_moment
Maxwell's theorem Moment-generating function Factorial moment generating function Negative probability Probability-generating function Vysochanskiï–Petunin
List_of_probability_topics
Statistical model allowing for frequent zero values
{\displaystyle G(z)=\sum \limits _{n=0}^{\infty }P(Y=n)z^{n}} be the probability generating function of y i {\displaystyle y_{i}} . If p 0 = Pr ( Y = 0 ) > 0.5
Zero-inflated_model
Statistical probability Distribution for discrete event counts
called it "Hermite distribution" from the fact its probability function and the moment generating function can be expressed in terms of the coefficients of
Hermite_distribution
Discrete probability distribution
_{k=-\infty }^{\infty }p(k;\mu _{1},\mu _{2})=1.} We know that the probability generating function (pgf) for a Poisson distribution is: G ( t ; μ ) = e μ ( t
Skellam_distribution
Measure of the shape of a function
moment L-moment Method of moments (probability theory) Method of moments (statistics) Moment-generating function Moment measure Second moment method
Moment_(mathematics)
Probability distribution
this, we calculate the probability generating function GX of X, which is the composition of the probability generating functions GN and GY1. Using G N
Negative binomial distribution
Negative_binomial_distribution
Function related to statistics and probability theory
likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability of seeing
Likelihood_function
Expectation or average of the falling factorial of a random variable
non-negative integer-valued random variables, and arise in the use of probability-generating functions to derive the moments of discrete random variables. Factorial
Factorial_moment
Probability distribution
{1-p}{p(1-p)^{2}}}\right)\\&={\frac {1}{p^{2}(1-p)}}\end{aligned}}} The probability generating functions of geometric random variables X {\displaystyle X} and Y {\displaystyle
Geometric_distribution
Probability distribution
In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes
Binomial_distribution
Overview of and topical guide to probability
transforms) Probability-generating functions Moment-generating functions Laplace transforms and Laplace–Stieltjes transforms Characteristic functions A proof
Outline_of_probability
Probability plot correlation coefficient plot Probability space Probability theory Probability-generating function Probable error Probit Probit model Procedural
List_of_statistics_articles
Conditional probability used in Bayesian statistics
The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood
Posterior_probability
Statistical measure of how far values spread from their average
generator of random variable X {\displaystyle X} is discrete with probability mass function x 1 ↦ p 1 , x 2 ↦ p 2 , … , x n ↦ p n {\displaystyle x_{1}\mapsto
Variance
Measure of the asymmetry of random variables
Skewness in probability theory and statistics is a measure of the asymmetry of the probability distribution of a real-valued random variable about its
Skewness
Topics referred to by the same term
graphics language in the PGF/TikZ pair Precision guided firearm Probability-generating function Progressive Graphics File, a file format This disambiguation
PGF
Uniform distribution on an interval
than that it is contained in the distribution's support. The probability density function of the continuous uniform distribution is f ( x ) = { 1 b − a
Continuous uniform distribution
Continuous_uniform_distribution
Linear transform from the time domain to the frequency domain
series Generating function Generating function transformation Laplace transform Laurent series Least-squares spectral analysis Probability-generating function
Z-transform
Mathematical theory
mathematical theory of probability, the combinants cn of a random variable X are defined via the combinant-generating function G(t), which is defined
Combinant
Average value of a random variable
In probability theory, the expected value (also called expectation, mean, or first moment) is a generalization of the weighted average. The expected value
Expected_value
Smooth approximation of one-hot arg max
The softmax function, also known as softargmax or normalized exponential function, converts a tuple of K real numbers into a probability distribution over
Softmax_function
Kind of stochastic process
right-hand side of the equation is a probability generating function. Let h(z) be the ordinary generating function for pi: h ( z ) = p 0 + p 1 z + p 2
Branching_process
Kind of mathematical function
in the definition of the Lebesgue integral. In probability theory, a measurable function on a probability space is known as a random variable. Let ( X
Measurable_function
Type of random mathematical object
point process. The probability generating function of non-negative integer-valued random variable leads to the probability generating functional being defined
Poisson_point_process
Compound probability distribution
{\displaystyle M_{\pi }} is the moment generating function of the density. For the probability generating function, one obtains m X ( s ) = M π ( s − 1
Mixed_Poisson_distribution
Aspect of queueing theory
Policies can also be evaluated using a measure of fairness. The probability generating function of the stationary queue length distribution is given by the
M/G/1_queue
Probability distribution
distribution for a real-valued random variable. The general form of its probability density function is f ( x ) = 1 2 π σ 2 exp ( − ( x − μ ) 2 2 σ 2 ) . {\displaystyle
Normal_distribution
Algebraic structure of set algebra
In mathematical analysis and in probability theory, a σ-algebra ("sigma algebra") is part of the formalism for defining sets that can be measured. In
Σ-algebra
Class of distance functions defined between probability distributions
In probability theory, integral probability metrics are types of distance functions between probability distributions, defined by how well a class of functions
Integral_probability_metric
Interpretation of probability
The propensity theory of probability is a probability interpretation in which the probability is thought of as a physical propensity, disposition, or tendency
Propensity_probability
Statistical approximation method
approximation formula for any PDF or probability mass function of a distribution, based on the moment generating function. There is also a formula for the
Saddlepoint approximation method
Saddlepoint_approximation_method
Basic method for pseudo-random number sampling
sampling, i.e., for generating sample numbers at random from any probability distribution given its cumulative distribution function. Inverse transformation
Inverse_transform_sampling
Distribution function associated with the empirical measure of a sample
distribution function is an estimate of the cumulative distribution function that generated the points in the sample. It converges with probability 1 to that
Empirical distribution function
Empirical_distribution_function
Probability function
large deviations theory, a rate function is a function used to quantify the probabilities of rare events. Such functions are used to formulate large deviation
Rate_function
Model in probability theory
\chi _{F}} denotes the indicator function of the event F {\displaystyle F} . In Grimmett and Stirzaker's Probability and Random Processes, this last condition
Martingale (probability theory)
Martingale_(probability_theory)
Mathematical concept
in the sample space. A probability function, P {\displaystyle P} , which assigns, to each event in the event space, a probability, which is a number between
Probability_space
Probability distribution of the sum of random variables
convolution of probability distributions. Often the manipulation of integrals can be avoided by use of some type of generating function. Such methods can
Convolution of probability distributions
Convolution_of_probability_distributions
The Dirac delta function, although not strictly a probability distribution, is a limiting form of many continuous probability functions. It represents
List of probability distributions
List_of_probability_distributions
Probability distribution
to multiple variables is called a Dirichlet distribution. The probability density function (PDF) of the beta distribution, for 0 ≤ x ≤ 1 {\displaystyle
Beta_distribution
Type of probability space
map from the unit interval to the space of continuous functions. The theory of standard probability spaces was started by von Neumann in 1932 and shaped
Standard_probability_space
When the occurrence of one event does not affect the likelihood of another
Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes. Two events are independent, statistically
Independence (probability theory)
Independence_(probability_theory)
Aspect of probability theory
\left((\varphi _{X}(t))^{N}\right),\,} and hence, using the probability-generating function of the Poisson distribution, we have φ Y ( t ) = e λ ( φ X
Compound_Poisson_distribution
Probability distribution
the normal, binomial, gamma, and Poisson distributions. The probability density function (pdf) of an exponential distribution is f ( x ; λ ) = { λ e −
Exponential_distribution
Family of probability distributions
equivalent to the Tweedie compound Poisson–gamma distribution. The probability generating function for the PNB distribution is G ( s ) = exp [ λ α − 1 α ( θ
Tweedie_distribution
Average uncertainty in variable's states
very low probability event. The information content, also called the surprisal or self-information, of an event E {\displaystyle E} is a function that increases
Entropy_(information_theory)
Variance of random sum
derivation can be done elementarily using the chain rule and the probability-generating function. For each n ≥ 0 {\displaystyle n\geq 0} , let χ n {\displaystyle
Blackwell-Girshick_equation
Canadian mathematician
Micheal (2019). "Central limit theorems from the roots of probability generating functions". Advances in Mathematics. 358 106840. arXiv:1804.07696. doi:10
Julian_Sahasrabudhe
Philosophical interpretation of the axioms of probability
word "probability" has been used in a variety of ways since it was first applied to the mathematical study of games of chance. Does probability measure
Probability_interpretations
Monte Carlo algorithm
samples from any probability distribution with probability density P ( x ) {\displaystyle P(x)} , provided that we know a function f ( x ) {\displaystyle
Metropolis–Hastings_algorithm
R is the field of real numbers, then this is the probability-generating function of the probability distribution of N. Similarly, (5) and (6) yield and
Schuette–Nesbitt_formula
Discrete probability distribution
_{k=0}^{n}x^{k}{\binom {n}{k}}^{\nu }.} Then, the probability generating function, moment generating function and characteristic function are given, respectively, by: G
Conway–Maxwell–binomial distribution
Conway–Maxwell–binomial_distribution
Mathematical theory on random variables
Free probability is a mathematical theory that studies non-commutative random variables. The "freeness" or free independence property is the analogue
Free_probability
Notions of probabilistic convergence, applied to estimation and asymptotic analysis
In probability theory, there exist several different notions of convergence of sequences of random variables, including convergence in probability, convergence
Convergence of random variables
Convergence_of_random_variables
Variable representing a random phenomenon
variables need not be defined on the same probability space. Two random variables having equal moment generating functions have the same distribution. This provides
Random_variable
Probability distribution
In probability theory and statistics, the gamma distribution is a versatile two-parameter family of continuous probability distributions. The exponential
Gamma_distribution
Function whose graph is 0, then 1, then 0 again, in an almost-everywhere continuous way
the basis for a rectangular wave. The rect function has been introduced 1953 by Woodward in "Probability and Information Theory, with Applications to
Rectangular_function
Power series theorem in mathematics
functions of real-valued and non-negative sequences, such as probability-generating functions. In particular, it is useful in the theory of Galton–Watson
Abel's_theorem
Probability distribution
physicist Eugene Wigner, is the probability distribution defined on the domain [−R, R] whose probability density function f is a scaled semicircle, i.e
Wigner semicircle distribution
Wigner_semicircle_distribution
Probability distribution
half-plane. It is one of the few stable distributions with a probability density function that can be expressed analytically, the others being the normal
Cauchy_distribution
Mathematical function having a characteristic S-shaped curve or sigmoid curve
common probability distributions are sigmoidal. One such example is the error function, which is related to the cumulative distribution function of a normal
Sigmoid_function
Exponentially decreasing bounds on tail distributions of random variables
In probability theory, a Chernoff bound is an exponentially decreasing upper bound on the tail of a random variable based on its moment generating function
Chernoff_bound
Fundamental result in the theory of large deviations
the theory of large deviations, a subdiscipline of probability theory. It determines the rate function of a series of iid random variables. A weak version
Cramér's theorem (large deviations)
Cramér's_theorem_(large_deviations)
Model for the extinction of family names
The process can be treated analytically using the method of probability generating functions. If the number of children ξ j {\displaystyle \xi _{j}} at
Galton–Watson_process
Continuous probability distribution
cumulative distribution function is a stretched exponential function. The Weibull distribution is related to a number of other probability distributions; in
Weibull_distribution
Mathematical identity in queueing theory
{\text{Var}}(S)}{2(\mu -\lambda )}}.} Writing π(z) for the probability-generating function of the number of customers in the queue π ( z ) = ( 1 − z )
Pollaczek–Khinchine_formula
Wigner distribution function in physics as opposed to in signal processing
Schrödinger equation to a probability distribution in phase space. It is a generating function for all spatial autocorrelation functions of a given quantum-mechanical
Wigner quasiprobability distribution
Wigner_quasiprobability_distribution
Probabilistic optimization technique and metaheuristic
{\displaystyle s_{\mathrm {new} }} is specified by an acceptance probability function P ( e , e n e w , T ) {\displaystyle P(e,e_{\mathrm {new} },T)}
Simulated_annealing
Problem in probability theory
z {\displaystyle z} with 1 + z {\displaystyle 1+z} in the probability generating function produces the o.g.f. for E [ ( X k ) ] {\displaystyle E\left[{X
Coupon_collector's_problem
contents. Probability theory Random variable Continuous probability distribution / (1:C) Cumulative distribution function / (1:DCR) Discrete probability distribution /
Catalog of articles in probability theory
Catalog_of_articles_in_probability_theory
Statistical model for a binary dependent variable
probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that
Logistic_regression
Mapping arbitrary data to fixed-size values
minimize duplication of output values (collisions). Hash functions rely on generating favorable probability distributions for their effectiveness, reducing access
Hash_function
Statistical function that converts a probability to a standard normal score
In statistics, the probit function converts a probability (a number between 0 and 1) into a score. This score indicates how many standard deviations a
Probit
Generating pseudo-random numbers that follow a probability distribution
number sampling is the numerical practice of generating pseudo-random numbers (PRN) that follow a given probability distribution. Methods are typically based
Non-uniform random variate generation
Non-uniform_random_variate_generation
Mathematical function
controls the width of the "bell". Gaussian functions are often used to represent the probability density function of a normally distributed random variable
Gaussian_function
Statistical sequence characterizing probability distributions
L-moments are a sequence of statistics used to summarize the shape of a probability distribution. They are linear combinations of order statistics (L-statistics)
L-moment
Theory of behavioral economics
It introduces a value function defined over gains and losses rather than final wealth, as well as a probability-weighting function that reflects the tendency
Prospect_theory
The discrete-stable distributions are defined through their probability-generating function G ( s | ν , a ) = ∑ n = 0 ∞ P ( N | ν , a ) ( 1 − s ) N = exp
Discrete-stable_distribution
Probability distribution
moment-generating function is actually undefined. Like all stable distributions except the normal distribution, the wing of the probability density function
Lévy_distribution
Probability distribution
{\displaystyle \operatorname {Laplace} (\mu ,b)} distribution if its probability density function is f ( x ∣ μ , b ) = 1 2 b e − | x − μ | b , {\displaystyle f(x\mid
Laplace_distribution
Probability distribution
ideal gas (chi distribution with three degrees of freedom). The probability density function (pdf) of the chi-distribution is f ( x ; k ) = { x k − 1 e −
Chi_distribution
Concept in probability theory
In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities. It
Law_of_total_probability
constructing the probability distribution of S. In the following W N ( x ) {\displaystyle W_{N}(x)\,} denotes the probability generating function of N: for this
Panjer_recursion
Halting probability of a random computer program
definition of a halting probability relies on the existence of a prefix-free universal computable function. Such a function, intuitively, represents
Chaitin's_constant
PROBABILITY GENERATING-FUNCTION
PROBABILITY GENERATING-FUNCTION
Boy/Male
Indian, Punjabi, Sikh
New Generation
Boy/Male
Tamil
Young generation
Boy/Male
Muslim
Old generation
Boy/Male
British, Czech, Hindu, Indian
New Generation
Girl/Female
Biblical
Birth, generation.
Girl/Female
Indian, Tamil
Generation
Boy/Male
Hindu, Indian
Young Generation
Boy/Male
Gujarati, Hindu, Indian, Kannada
Era; Generation
Girl/Female
Biblical
A generation.
Boy/Male
Tamil
Forthcoming generation
Boy/Male
Bengali, Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Punjabi
Generation; Coming Generation of Father; Family
Boy/Male
Indian, Modern
Generations
Girl/Female
Biblical
Generation, habitation.
Girl/Female
Indian
Generation
Boy/Male
Biblical
Nativity, generation.
Boy/Male
Biblical, British, English
Nativity; Generation
Boy/Male
Indian
Young Generation
Girl/Female
Biblical
Nativity, generation.
Boy/Male
Japanese Welsh
Large; generation.
Boy/Male
Biblical
Nativity, generation.
PROBABILITY GENERATING-FUNCTION
PROBABILITY GENERATING-FUNCTION
Girl/Female
Muslim
Girl/Female
Indian
Greenery, Greenness, Vagrancy
Boy/Male
Hindu
Principal
Surname or Lastname
English
English : topographic name for someone who lived by an oak tree, from misdivision of Middle English atten oke ‘at the oak’.South German (also Nöck) : from Tyrolean nock, nog ‘rounded hill’, ‘rock’, hence a topographic name for someone who lived by such a feature, or a nickname from the same word used in the sense ‘short and fat’.
Boy/Male
American, Australian, British, English
Cheerful
Girl/Female
Indian
Illuminating, Shedding light, Bright and shining
Girl/Female
Arabic, Muslim
Name of a Sahabiyyah RA
Boy/Male
American, Australian, British, English, German, Irish, Teutonic
Friend in Battle; Friend with a Spear; Rough
Girl/Female
Indian, Tamil
Not Known
Boy/Male
American, Australian, Christian, Gaelic, Irish
Handsome; Young Animal; Good Looking Lad
PROBABILITY GENERATING-FUNCTION
PROBABILITY GENERATING-FUNCTION
PROBABILITY GENERATING-FUNCTION
PROBABILITY GENERATING-FUNCTION
PROBABILITY GENERATING-FUNCTION
n.
Likelihood; probability.
n.
Probability; verisimilitude.
n.
Probability.
a.
Pertaining to generation, or to the generative organs.
a.
Generating mucus.
a.
Generating bile.
n.
Likelihood of the occurrence of any event in the doctrine of chances, or the ratio of the number of favorable chances to the whole number of chances, favorable and unfavorable. See 1st Chance, n., 5.
n.
Probability; likelihood.
pl.
of Probability
a.
Having the power of entering, piercing, or pervading; sharp; subtile; penetrative; as, a penetrating odor.
n.
Probability.
n.
One who maintains that a man may do that which has a probability of being right, or which is inculcated by teachers of authority, although other opinions may seem to him still more probable.
a.
Having the power of generating, propagating, originating, or producing.
n.
The act of generating or begetting; procreation, as of animals.
n.
The doctrine of the probabilists.
a.
Acute; discerning; sagacious; quick to discover; as, a penetrating mind.
n.
Origination by some process, mathematical, chemical, or vital; production; formation; as, the generation of sounds, of gases, of curves, etc.
superl.
Having probability; affording probability; probable; likely.
adv.
In all probability; probably.
n.
One who maintains that certainty is impossible, and that probability alone is to govern our faith and actions.