Faster access than browser!Similarities between Expected value and Outline of probability
Expected value and Outline of probability have 26 things in common (in Unionpedia): Almost surely, Berry–Esseen theorem, Cauchy distribution, Central moment, Characteristic function (probability theory), Chebyshev's inequality, Conditional expectation, Covariance, Cumulative distribution function, Dominated convergence theorem, Event (probability theory), Fatou's lemma, Independence (probability theory), Jensen's inequality, Law of large numbers, Law of total expectation, Markov's inequality, Moment-generating function, Monotone convergence theorem, Probability density function, Probability distribution, Probability measure, Probability space, Probability theory, Random variable, Variance.
Almost surely
In probability theory, one says that an event happens almost surely (sometimes abbreviated as a.s.) if it happens with probability one.
Almost surely and Expected value · Almost surely and Outline of probability ·
Berry–Esseen theorem
In probability theory, the central limit theorem states that, under certain circumstances, the probability distribution of the scaled mean of a random sample converges to a normal distribution as the sample size increases to infinity.
Berry–Esseen theorem and Expected value · Berry–Esseen theorem and Outline of probability ·
Cauchy distribution
The Cauchy distribution, named after Augustin Cauchy, is a continuous probability distribution.
Cauchy distribution and Expected value · Cauchy distribution and Outline of probability ·
Central moment
In probability theory and statistics, a central moment is a moment of a probability distribution of a random variable about the random variable's mean; that is, it is the expected value of a specified integer power of the deviation of the random variable from the mean.
Central moment and Expected value · Central moment and Outline of probability ·
Characteristic function (probability theory)
In probability theory and statistics, the characteristic function of any real-valued random variable completely defines its probability distribution.
Characteristic function (probability theory) and Expected value · Characteristic function (probability theory) and Outline of probability ·
Chebyshev's inequality
In probability theory, Chebyshev's inequality (also spelled as Tchebysheff's inequality, Нера́венство Чебышёва, also called Bienaymé-Chebyshev inequality) guarantees that, for a wide class of probability distributions, no more than a certain fraction of values can be more than a certain distance from the mean.
Chebyshev's inequality and Expected value · Chebyshev's inequality and Outline of probability ·
Conditional expectation
In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value – the value it would take “on average” over an arbitrarily large number of occurrences – given that a certain set of "conditions" is known to occur.
Conditional expectation and Expected value · Conditional expectation and Outline of probability ·
Covariance
In probability theory and statistics, covariance is a measure of the joint variability of two random variables.
Covariance and Expected value · Covariance and Outline of probability ·
Cumulative distribution function
In probability theory and statistics, the cumulative distribution function (CDF, also cumulative density function) of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x. In the case of a continuous distribution, it gives the area under the probability density function from minus infinity to x. Cumulative distribution functions are also used to specify the distribution of multivariate random variables.
Cumulative distribution function and Expected value · Cumulative distribution function and Outline of probability ·
Dominated convergence theorem
In measure theory, Lebesgue's dominated convergence theorem provides sufficient conditions under which almost everywhere convergence of a sequence of functions implies convergence in the L1 norm.
Dominated convergence theorem and Expected value · Dominated convergence theorem and Outline of probability ·
Event (probability theory)
In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned.
Event (probability theory) and Expected value · Event (probability theory) and Outline of probability ·
Fatou's lemma
In mathematics, Fatou's lemma establishes an inequality relating the Lebesgue integral of the limit inferior of a sequence of functions to the limit inferior of integrals of these functions.
Expected value and Fatou's lemma · Fatou's lemma and Outline of probability ·
Independence (probability theory)
In probability theory, two events are independent, statistically independent, or stochastically independent if the occurrence of one does not affect the probability of occurrence of the other.
Expected value and Independence (probability theory) · Independence (probability theory) and Outline of probability ·
Jensen's inequality
In mathematics, Jensen's inequality, named after the Danish mathematician Johan Jensen, relates the value of a convex function of an integral to the integral of the convex function.
Expected value and Jensen's inequality · Jensen's inequality and Outline of probability ·
Law of large numbers
In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times.
Expected value and Law of large numbers · Law of large numbers and Outline of probability ·
Law of total expectation
The proposition in probability theory known as the law of total expectation, the law of iterated expectations, the tower rule, and the smoothing theorem, among other names, states that if X is a random variable whose expected value \operatorname(X) is defined, and Y is any random variable on the same probability space, then i.e., the expected value of the conditional expected value of X given Y is the same as the expected value of X. One special case states that if _i is a finite or countable partition of the sample space, then.
Expected value and Law of total expectation · Law of total expectation and Outline of probability ·
Markov's inequality
In probability theory, Markov's inequality gives an upper bound for the probability that a non-negative function of a random variable is greater than or equal to some positive constant.
Expected value and Markov's inequality · Markov's inequality and Outline of probability ·
Moment-generating function
In probability theory and statistics, the moment-generating function of a real-valued random variable is an alternative specification of its probability distribution.
Expected value and Moment-generating function · Moment-generating function and Outline of probability ·
Monotone convergence theorem
In the mathematical field of real analysis, the monotone convergence theorem is any of a number of related theorems proving the convergence of monotonic sequences (sequences that are increasing or decreasing) that are also bounded.
Expected value and Monotone convergence theorem · Monotone convergence theorem and Outline of probability ·
Probability density function
In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function, whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample.
Expected value and Probability density function · Outline of probability and Probability density function ·
Probability distribution
In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.
Expected value and Probability distribution · Outline of probability and Probability distribution ·
Probability measure
In mathematics, a probability measure is a real-valued function defined on a set of events in a probability space that satisfies measure properties such as countable additivity.
Expected value and Probability measure · Outline of probability and Probability measure ·
Probability space
In probability theory, a probability space or a probability triple (\Omega, \mathcal, P) is a mathematical construct that models a real-world process (or “experiment”) consisting of states that occur randomly.
Expected value and Probability space · Outline of probability and Probability space ·
Probability theory
Probability theory is the branch of mathematics concerned with probability.
Expected value and Probability theory · Outline of probability and Probability theory ·
Random variable
In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.
Expected value and Random variable · Outline of probability and Random variable ·
Variance
In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.
Expected value and Variance · Outline of probability and Variance ·
The list above answers the following questions
- What Expected value and Outline of probability have in common
- What are the similarities between Expected value and Outline of probability
Expected value and Outline of probability Comparison
Expected value has 102 relations, while Outline of probability has 143. As they have in common 26, the Jaccard index is 10.61% = 26 / (102 + 143).
References
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