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searching for Probability mass function 36 found (183 total)

alternate case: probability mass function

(a,b,0) class of distributions (797 words) [view diff] exact match in snippet view article

random variable N whose values are nonnegative integers whose probability mass function satisfies the recurrence formula p k p k − 1 = a + b k , k = 1

Displaced Poisson distribution (646 words) [view diff] exact match in snippet view article find links to article

distribution, is a generalization of the Poisson distribution. The probability mass function is P ( X = n ) = { e − λ λ n + r ( n + r ) ! ⋅ 1 I ( r , λ )

Saddlepoint approximation method (790 words) [view diff] exact match in snippet view article find links to article

provides a highly accurate approximation formula for any PDF or probability mass function of a distribution, based on the moment generating function. There

Credal network (511 words) [view diff] exact match in snippet view article find links to article

variables given their parents. As a Bayesian network defines a joint probability mass function over its variables, a credal network defines a joint credal set

Singular distribution (195 words) [view diff] exact match in snippet view article find links to article

distributions can be described as a discrete distribution (with a probability mass function), an absolutely continuous distribution (with a probability density)

Probability distribution fitting (1,911 words) [view diff] exact match in snippet view article find links to article

of the newly obtained probability mass function can also be determined. The variance for a Bayesian probability mass function can be defined as σ P θ

Conditional probability distribution (2,150 words) [view diff] exact match in snippet view article find links to article

included variables. For discrete random variables, the conditional probability mass function of Y {\displaystyle Y} given X = x {\displaystyle X=x} can be

Prediction by partial matching (801 words) [view diff] exact match in snippet view article find links to article

In many compression algorithms, the ranking is equivalent to probability mass function estimation. Given the previous letters (or given a context), each

Noncentral beta distribution (812 words) [view diff] exact match in snippet view article find links to article

where λ is the noncentrality parameter, P(.) is the Poisson(λ/2) probability mass function, \alpha=m/2 and \beta=n/2 are shape parameters, and I x ( a ,

Data processing inequality (439 words) [view diff] exact match in snippet view article find links to article

{\displaystyle X} . Specifically, we have such a Markov chain if the joint probability mass function can be written as p ( x , y , z ) = p ( x ) p ( y | x ) p ( z

M/M/∞ queue (946 words) [view diff] exact match in snippet view article find links to article

can be expressed in terms of Kummer's function. The stationary probability mass function is a Poisson distribution π k = ( λ / μ ) k e − λ / μ k ! k ≥

Harris chain (1,079 words) [view diff] exact match in snippet view article find links to article

probabilities P[Xn+1 = y | Xn = x] for x,y ∈ Ω. The measure ρ is a probability mass function on the states, so that ρ(x) ≥ 0 for all x ∈ Ω, and the sum of

Galton board (1,572 words) [view diff] exact match in snippet view article find links to article

k {\displaystyle {n \choose k}p^{k}(1-p)^{n-k}} . This is the probability mass function of a binomial distribution. The number of rows correspond to the

Law of large numbers (6,355 words) [view diff] exact match in snippet view article find links to article

numbers, one could easily obtain the probability mass function. For each event in the objective probability mass function, one could approximate the probability

Noncentral hypergeometric distributions (2,261 words) [view diff] exact match in snippet view article find links to article

Probability mass function for Wallenius' noncentral hypergeometric distribution for different values of the odds ratio ω. m1 = 80, m2 = 60, n = 100, ω

Probability theory (3,589 words) [view diff] exact match in snippet view article find links to article

point in the sample space to the "probability" value is called a probability mass function abbreviated as pmf. Continuous probability theory deals with events

Empirical Bayes method (2,731 words) [view diff] exact match in snippet view article find links to article

\over {p_{G}(y_{i})}},} where pG is the marginal probability mass function obtained by integrating out θ over G. To take advantage of this

Information theory and measure theory (1,762 words) [view diff] exact match in snippet view article find links to article

{\displaystyle \Omega } a finite set, f {\displaystyle f} is a probability mass function on Ω {\displaystyle \Omega } , and ν {\displaystyle \nu } is the

Covariance (4,742 words) [view diff] exact match in snippet view article find links to article

{\displaystyle X} and Y {\displaystyle Y} have the following joint probability mass function, in which the six central cells give the discrete joint probabilities

Exponential family random graph models (3,620 words) [view diff] exact match in snippet view article find links to article

ERGM on a set of graphs Y {\displaystyle {\mathcal {Y}}} with probability mass function P ( Y = y | θ ) = exp ⁡ ( θ T s ( y ) ) c ( θ ) {\displaystyle

Hyperbolastic functions (7,011 words) [view diff] exact match in snippet view article find links to article

Y}{P(y)\ {log}_{b}P(y)}} where P ( y ) {\displaystyle P(y)} is the probability mass function for the random variable Y {\displaystyle Y} . The information

Von Mises–Fisher distribution (4,887 words) [view diff] exact match in snippet view article find links to article

the concentration is κ ≥ 0 {\displaystyle \kappa \geq 0} . The probability mass function, for x ∈ { − 1 , 1 } {\displaystyle x\in \{-1,1\}} is: f 1 ( x

Binomial theorem (6,735 words) [view diff] exact match in snippet view article find links to article

is equal to e. The binomial theorem is closely related to the probability mass function of the negative binomial distribution. The probability of a (countable)

Gillespie algorithm (3,119 words) [view diff] exact match in snippet view article find links to article

single Gillespie simulation represents an exact sample from the probability mass function that is the solution of the master equation. The physical basis

Wilcoxon signed-rank test (6,439 words) [view diff] exact match in snippet view article find links to article

− n {\displaystyle t^{+}-n} . Under the null hypothesis, the probability mass function of T + {\displaystyle T^{+}} satisfies Pr ( T + = t + ) = u n

Gini coefficient (10,898 words) [view diff] exact match in snippet view article find links to article

Gini coefficient. For a discrete probability distribution with probability mass function f ( y i ) , {\displaystyle f(y_{i}),} i = 1 , … , n {\displaystyle

Otsu's method (3,790 words) [view diff] exact match in snippet view article find links to article

of pixels in the image N {\displaystyle N} , defines the joint probability mass function in a 2-dimensional histogram: P i j = f i j N , ∑ i = 0 L − 1

Introduction to entropy (5,257 words) [view diff] exact match in snippet view article find links to article

entropy is a measure of the "spread" of a probability density or probability mass function. Thermodynamics makes no assumptions about the atomistic nature

Entropy (information theory) (10,194 words) [view diff] exact match in snippet view article

entropy H ( p ) {\displaystyle \mathrm {H} (p)} is concave in the probability mass function p {\displaystyle p} , i.e.: 30 H ( λ p 1 + ( 1 − λ ) p 2 ) ≥

Mutual information (8,724 words) [view diff] exact match in snippet view article find links to article

sum:: 20 where P ( X , Y ) {\displaystyle P_{(X,Y)}} is the joint probability mass function of X {\displaystyle X} and Y {\displaystyle Y} , and P X {\displaystyle

Conditional expectation (6,271 words) [view diff] exact match in snippet view article find links to article

where P ( X = x , Y = y ) {\displaystyle P(X=x,Y=y)} is the joint probability mass function of X and Y. The sum is taken over all possible outcomes of X.

Vector generalized linear model (4,746 words) [view diff] exact match in snippet view article find links to article

consists of four elements: 1. A probability density function or probability mass function from some statistical distribution which has a log-likelihood

Generalized functional linear model (2,869 words) [view diff] exact match in snippet view article find links to article

exponential family, then its probability density function or probability mass function (as the case may be) is f ( y i ∣ X i ) = exp ⁡ ( y i θ i − b

Stochastic dynamic programming (5,371 words) [view diff] exact match in snippet view article find links to article

{float} -- target wealth pmf {List[List[Tuple[int, float]]]} -- probability mass function """ # initialize instance variables self.bettingHorizon, self

Backpressure routing (7,659 words) [view diff] exact match in snippet view article find links to article

{\displaystyle \pi _{S}} is a probability distribution, not a probability mass function). A general algorithm for the network observes S(t) every slot

Stable count distribution (7,739 words) [view diff] exact match in snippet view article find links to article

probability density function of a Gamma distribution (here) and the probability mass function of a Poisson distribution (here, s → s + 1 {\displaystyle s\rightarrow