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Probability measure

A probability measure is a countably additive set function P defined on a \sigma-algebra \mathcal{F} of subsets of a sample space \Omega, taking values in [0, 1], with P(\Omega) = 1 and P(\emptyset) = 0, quantifying the likelihood of events in a rigorous mathematical framework.^[1]^[2] This structure ensures non-negativity (P(A) \geq 0 for all A \in \mathcal{F}), countable additivity (for disjoint events A_i, P(\bigcup_i A_i) = \sum_i P(A_i)), and normalization to total probability 1, forming the core of measure-theoretic probability.^[1]^[2] In this context, a probability space is the triple (\Omega, \mathcal{F}, P), where \Omega represents all possible outcomes of a random experiment, \mathcal{F} is a \sigma-algebra containing the measurable events (closed under complements and countable unions), and P is the probability measure assigning probabilities consistently.^[1]^[2] Key properties derived from these axioms include finite additivity for finite disjoint unions, the complement rule P(A^c) = 1 - P(A), monotonicity (A \subset B implies P(A) \leq P(B)), and the union bound P(\bigcup_i A_i) \leq \sum_i P(A_i).^[1] This setup extends classical probability to uncountable spaces, such as the real line with Lebesgue measure, enabling the definition of random variables as measurable functions from \Omega to a measurable space.^[2] The modern axiomatic foundation of probability measures was established by Andrey Nikolaevich Kolmogorov in his 1933 monograph Foundations of the Theory of Probability, which reformulated probability as a special case of measure theory to provide a rigorous, abstract treatment free from intuitive but imprecise notions like equally likely outcomes.^[3]^[4] Kolmogorov's three axioms—non-negativity, countable additivity, and normalization—directly define the probability measure and resolved foundational issues in early probability theory, such as handling infinite sample spaces and ensuring consistency with limit theorems.^[2]^[5] Prior to this, probability developed heuristically from games of chance in the 17th century by Pascal and Fermat, but lacked a unified mathematical structure until measure theory's maturation in the early 20th century.^[3] Probability measures underpin advanced stochastic processes, statistical inference, and applications in fields like physics, finance, and machine learning, where they model uncertainty over continuous or complex domains, such as Brownian motion or risk assessment via expected values \mathbb{E}[X] = \int X \, dP.^[2] They facilitate convergence concepts like almost sure convergence (events of probability 1) and the law of large numbers, essential for empirical validation of probabilistic models.^[2] This framework remains the standard in contemporary probability theory, influencing developments in ergodic theory and information theory.^[6]

Background Concepts

Measurable Spaces

A measurable space is defined as a pair (\Omega, \Sigma), where \Omega is a nonempty set known as the sample space, and \Sigma is a \sigma-algebra of subsets of \Omega. This structure provides the foundational framework for assigning measures to subsets of \Omega in a consistent manner.^[7]^[8] A \sigma-algebra \Sigma on \Omega is a collection of subsets satisfying specific closure properties: it contains the empty set \emptyset and \Omega itself; it is closed under complementation, meaning that if A \in \Sigma, then \Omega \setminus A \in \Sigma; and it is closed under countable unions, so if A_1, A_2, \dots \in \Sigma, then \bigcup_{n=1}^\infty A_n \in \Sigma. Closure under countable unions implies closure under countable intersections via De Morgan's laws. These properties ensure that \Sigma forms a Boolean algebra extended to handle infinite operations, allowing the identification of "events" as elements of \Sigma.^[9]^[10] Examples of \sigma-algebras illustrate their construction. For a finite sample space \Omega, the power set $2^\Omega—the collection of all subsets of \Omega—serves as a \sigma-algebra, as it trivially satisfies the required properties. In the case of the uncountable space \Omega = \mathbb{R}, the Borel \sigma-algebra \mathcal{B}(\mathbb{R}) is the smallest \sigma-algebra containing all open intervals (a, b) for a, b \in \mathbb{R}; it is generated by taking all countable unions, intersections, and complements starting from these intervals.^[9]^[11] In probability theory, measurable spaces play a crucial role when dealing with uncountable sample spaces, such as the real line, by restricting attention to a collection of subsets in \Sigma that can be deemed "measurable" in a well-defined way, thereby avoiding inconsistencies that arise with arbitrary subsets. This selection enables the systematic treatment of events in continuous models without encompassing non-constructive or pathological sets.^[12]^[13]

General Measures

A measure on a measurable space (X, \Sigma) is a function \mu: \Sigma \to [0, \infty] that assigns a non-negative extended real number to each measurable set, satisfying two key properties: \mu(\emptyset) = 0 and countable additivity, meaning that for any countable collection of pairwise disjoint sets \{E_i\}_{i=1}^\infty \subset \Sigma, \mu\left(\bigcup_{i=1}^\infty E_i\right) = \sum_{i=1}^\infty \mu(E_i).^[14] This framework builds on the σ-algebra \Sigma, which provides the collection of subsets of X deemed "measurable."^[14] Measures differ from related concepts like pre-measures and outer measures. A pre-measure is typically defined on an algebra (a collection closed under finite unions and complements) rather than a full σ-algebra, and it satisfies countable additivity only when the union remains in the algebra.^[14] An outer measure, in contrast, extends to the power set of X and is countably subadditive but not necessarily additive on non-measurable sets.^[14] Classic examples illustrate these properties. The Lebesgue measure on \mathbb{R}^n assigns to each Borel measurable set the intuitive notion of n-dimensional volume, such as \mu([0,1]) = 1, and extends additively to disjoint unions like intervals.^[14] The counting measure on a countable set, such as the natural numbers, defines \mu(E) as the cardinality of E (or \infty if infinite), which is countably additive since the union of disjoint finite sets has cardinality equal to the sum of their sizes.^[14] Constructing measures often involves extending pre-measures via theorems like Carathéodory's extension theorem. This theorem states that given a pre-measure \mu_0 on a semi-ring or algebra \mathcal{A} of subsets of X, one can define an outer measure \mu^* on the power set by \mu^*(E) = \inf\left\{\sum \mu_0(A_j) : E \subset \bigcup A_j, A_j \in \mathcal{A}\right\}, and the \mu^*-measurable sets form a σ-algebra containing \sigma(\mathcal{A}) on which \mu^* restricts to a measure \mu extending \mu_0.^[15] Uniqueness holds under σ-finiteness: if X = \bigcup_{j=1}^\infty A_j with \mu(A_j) < \infty, any other measure agreeing with \mu_0 on \mathcal{A} coincides with \mu on the generated σ-algebra.^[15]^[16]

Definition

Formal Definition

A probability measure P on a measurable space (\Omega, \Sigma), where \Omega is the sample space and \Sigma is a \sigma-algebra of subsets of \Omega, is a function P: \Sigma \to [0,1] that assigns to each event E \in \Sigma a number P(E) representing the probability of E, satisfying P(\Omega) = 1 and the axiom of countable additivity: for any countable collection of pairwise disjoint events \{E_i\}_{i=1}^\infty \subset \Sigma,

P\left( \bigcup_{i=1}^\infty E_i \right) = \sum_{i=1}^\infty P(E_i).

^[17] This formulation distinguishes a probability measure from a general measure by its normalization to total mass 1, ensuring probabilities lie between 0 and 1 inclusive.^[17] The modern measure-theoretic foundation of probability theory rests on these axioms for probability measures, providing a rigorous framework that unifies classical probability with abstract measure theory.^[17] This axiomatic approach was introduced by Andrey Kolmogorov in his 1933 monograph Grundbegriffe der Wahrscheinlichkeitsrechnung to place probability on a solid mathematical footing, resolving earlier inconsistencies in the field.^[17]

Probability Spaces

A probability space is formally defined as a triple (\Omega, \Sigma, P), where \Omega is a nonempty set known as the sample space, \Sigma is a \sigma-algebra of subsets of \Omega (the events), and P: \Sigma \to [0,1] is a probability measure on (\Omega, \Sigma) satisfying Kolmogorov's axioms: P(A) \geq 0 for all A \in \Sigma, P(\Omega) = 1, and countable additivity for disjoint events.^[17] This structure provides the foundational framework for modern probability theory, as established by Andrey Kolmogorov in his axiomatic approach.^[17] In this triple, \Omega represents the set of all possible outcomes of a random experiment, \Sigma specifies the collection of measurable events (subsets to which probabilities can be assigned), and P quantifies the likelihood of each event occurring, with P(A) interpreted as the probability of event A \in \Sigma.^[18] This setup models random experiments by capturing uncertainty in a rigorous mathematical manner: for instance, tossing a die corresponds to \Omega = \{1, 2, 3, 4, 5, 6\}, \Sigma as the power set, and P assigning equal measure $1/6 to each singleton, enabling the computation of probabilities for composite events like sums or sequences of trials.^[17] For continuous cases, standard probability spaces often assume completeness and separability to ensure desirable properties like the existence of regular conditional probabilities and compatibility with stochastic processes.^[19]^[20] Completeness means that if a set N \in \Sigma has P(N) = 0, then every subset of N is also in \Sigma with measure zero; separability typically requires \Omega to be a complete separable metric space (Polish space), with \Sigma the completion of the Borel \sigma-algebra, facilitating measure-theoretic constructions such as Lebesgue measure on [0,1].^[19] Within a probability space, two events A, B \in \Sigma are considered equivalent almost surely if their symmetric difference has probability zero, i.e., P(A \triangle B) = 0, meaning they differ only by a null set under P.^[18] This notion allows identification of events up to negligible discrepancies, which is crucial for theorems like the almost sure convergence in the law of large numbers, where properties hold except on sets of measure zero.^[18]

Properties

Axiomatic Properties

A probability measure P is defined within the framework of a probability space (\Omega, \Sigma, P), where \Omega is the sample space, \Sigma is a \sigma-algebra of events, and P: \Sigma \to [0,1] satisfies a set of axioms established by Andrey Kolmogorov.^[17] These axioms provide the rigorous mathematical foundation for probability theory, ensuring consistency with intuitive notions of chance while enabling the handling of infinite sample spaces.^[17] The first axiom is non-negativity, which requires that P(E) \geq 0 for every event E \in \Sigma.^[17] This property guarantees that probabilities cannot be negative, aligning with their interpretation as measures of likelihood or relative frequency, and it forms the basis for treating probability as a non-negative set function similar to mass or volume.^[21] In Kolmogorov's formulation, this axiom applies universally to all measurable events, preventing paradoxes that could arise from negative values in probabilistic reasoning.^[17] The second axiom is normalization, stating that P(\Omega) = 1.^[17] This condition specifies that the entire sample space has total probability 1, representing certainty, and it implies that P(\emptyset) = 0 as a direct consequence when combined with the other axioms, since the empty set contributes no probability mass.^[21] Normalization ensures that probabilities are calibrated on a scale from 0 to 1, facilitating comparisons and normalizations across different probabilistic models.^[17] The third axiom is countable additivity, which asserts that if \{E_i\}_{i=1}^\infty is a countable collection of pairwise disjoint events in \Sigma (i.e., E_i \cap E_j = \emptyset for all i \neq j), then

P\left( \bigcup_{i=1}^\infty E_i \right) = \sum_{i=1}^\infty P(E_i).

^[17] This axiom generalizes the intuitive idea that the probability of a union of mutually exclusive events equals the sum of their individual probabilities, extending it from finite to countably infinite collections to accommodate complex spaces like those in continuous probability.^[21] It is essential for ensuring the measure's behavior under limits and infinite partitions, providing the analytic power needed for advanced theorems in probability.^[17] As a consequence of countable additivity, finite additivity holds: for any finite collection of pairwise disjoint events \{E_1, \dots, E_n\} \subset \Sigma,

P\left( \bigcup_{i=1}^n E_i \right) = \sum_{i=1}^n P(E_i),

which follows by setting P(E_{n+1}) = P(E_{n+2}) = \cdots = 0 in the countable case.^[21] This derived property confirms the consistency of the axioms for practical, finite-event scenarios while underscoring the strength of the countable version in theoretical developments.^[17]

Derived Properties

Probability measures exhibit several important derived properties that stem from their axiomatic structure, enabling the analysis of complex events through limits and inequalities. These properties facilitate computations and proofs in probability theory by extending the basic axioms to broader classes of set operations. One fundamental derived property is monotonicity. For any measurable sets E and F with E \subseteq F, it holds that P(E) \leq P(F). This follows from the disjoint union F = E \cup (F \setminus E), yielding P(F) = P(E) + P(F \setminus E) and P(F \setminus E) \geq 0 by non-negativity.^[22] Another key property is subadditivity, which bounds the probability of a union by the sum of individual probabilities. For any countable collection of measurable sets \{E_i\}_{i=1}^\infty,

P\left( \bigcup_{i=1}^\infty E_i \right) \leq \sum_{i=1}^\infty P(E_i).

To derive this, construct disjoint sets D_1 = E_1 and D_k = E_k \setminus \bigcup_{i=1}^{k-1} E_i for k \geq 2, so \bigcup_{i=1}^\infty E_i = \bigcup_{k=1}^\infty D_k and countable additivity gives P\left( \bigcup_{i=1}^\infty E_i \right) = \sum_{k=1}^\infty P(D_k) \leq \sum_{k=1}^\infty P(E_k) by monotonicity.^[21] Probability measures also satisfy continuity from below. If \{E_n\}_{n=1}^\infty is an increasing sequence of measurable sets (i.e., E_1 \subseteq E_2 \subseteq \cdots) and E = \bigcup_{n=1}^\infty E_n, then

P(E) = \lim_{n \to \infty} P(E_n).

This is obtained by defining disjoint differences D_1 = E_1 and D_n = E_n \setminus E_{n-1} for n \geq 2, so E = \bigcup_{n=1}^\infty D_n and P(E) = \sum_{n=1}^\infty P(D_n) = \lim_{m \to \infty} \sum_{n=1}^m P(D_n) = \lim_{m \to \infty} P(E_m) via countable additivity and finite additivity.^[22] Dually, continuity from above holds for decreasing sequences. If \{F_n\}_{n=1}^\infty is a decreasing sequence of measurable sets (i.e., F_1 \supseteq F_2 \supseteq \cdots) with P(F_1) < \infty and F = \bigcap_{n=1}^\infty F_n, then

P(F) = \lim_{n \to \infty} P(F_n).

The proof applies continuity from below to the complements: since P(\Omega) = 1 < \infty, the sets \Omega \setminus F_n form an increasing sequence with union \Omega \setminus F, yielding \lim_{n \to \infty} P(\Omega \setminus F_n) = P(\Omega \setminus F), and subtracting from 1 gives the result.^[21] Finally, the inclusion-exclusion principle provides an exact formula for the probability of finite unions. For measurable sets E_1, \dots, E_n,

P\left( \bigcup_{i=1}^n E_i \right) = \sum_{i=1}^n P(E_i) - \sum_{1 \leq i < j \leq n} P(E_i \cap E_j) + \sum_{1 \leq i < j < k \leq n} P(E_i \cap E_j \cap E_k) - \cdots + (-1)^{n+1} P\left( \bigcap_{i=1}^n E_i \right).

This alternating sum arises iteratively from additivity and the decomposition of unions into disjoint parts, with the general term involving intersections over subsets of size k.^[23]

Examples

Discrete Probability Measures

A discrete probability measure is defined on a countable sample space \Omega, which may be finite or countably infinite, where the measure assigns a non-negative probability p(\omega) to each singleton \{\omega\} such that \sum_{\omega \in \Omega} p(\omega) = 1.^[1] This formulation ensures that the measure satisfies the axioms of a probability measure restricted to discrete spaces, with all probabilities concentrated on individual points. The function p: \Omega \to [0,1] is known as the probability mass function (PMF), which fully characterizes the discrete probability measure by specifying the probability at each point in \Omega.^[24] The PMF plays a central role in computations, as the probability of any event E \subseteq \Omega is obtained by summing the masses over the points in E:

P(E) = \sum_{\omega \in E} p(\omega).

This summation directly extends the general definition of a probability measure to the discrete case, where integration is replaced by discrete addition.^[25] Common examples of discrete probability measures include the uniform distribution on a finite set \{1, 2, \dots, n\}, where the PMF is p(k) = \frac{1}{n} for each k = 1, \dots, n, assigning equal probability to each outcome. The Bernoulli distribution, with sample space \{0, 1\} and PMF p(1) = p, p(0) = 1 - p for $0 < p < 1, models a single trial with two outcomes, such as success or failure.^[26] Another key example is the Poisson distribution, defined on \{0, 1, 2, \dots\} with PMF

p(k) = \frac{e^{-\lambda} \lambda^k}{k!}, \quad k = 0, 1, 2, \dots,

for parameter \lambda > 0, which approximates rare events in large populations.^[27] In each case, the PMF satisfies the normalization condition, enabling straightforward event probability calculations via summation.^[28]

Continuous Probability Measures

Absolutely continuous probability measures, a common type of continuous (atomless) probability measure on uncountable sample spaces such as the real line, are those absolutely continuous with respect to the Lebesgue measure, meaning that for any measurable set E, the measure P(E) = 0 whenever the Lebesgue measure of E is zero.^[29] In this case, the probability measure P admits a representation P(E) = \int_E f(x) \, dx, where f \geq 0 is the probability density function (PDF) satisfying \int_{-\infty}^{\infty} f(x) \, dx = 1.^[30] The cumulative distribution function (CDF) associated with such a measure is given by F(x) = P((-\infty, x]), which is continuous and non-decreasing, with F(x) = \int_{-\infty}^x f(t) \, dt.^[30] The existence of the density f follows from the Radon-Nikodym theorem, which guarantees a unique (up to null sets) non-negative integrable function f such that P is the indefinite integral of f with respect to Lebesgue measure, provided P is absolutely continuous relative to it.^[31] While most continuous probability measures encountered in applications are absolutely continuous, there also exist singular continuous measures, which are atomless but mutually singular with respect to Lebesgue measure (i.e., concentrated on a set of Lebesgue measure zero). A classic example is the Cantor distribution, whose cumulative distribution function is the Devil's staircase (Cantor function), supported on the Cantor set. Common examples of absolutely continuous probability measures include the uniform distribution on [a, b], with PDF f(x) = \frac{1}{b-a} for x \in [a, b] and 0 otherwise, which assigns equal probability density across the interval.^[32] The normal distribution N(\mu, \sigma^2) has PDF

f(x) = \frac{1}{\sqrt{2\pi \sigma^2}} \exp\left( -\frac{(x - \mu)^2}{2\sigma^2} \right),

representing a symmetric bell-shaped curve centered at \mu with spread \sigma > 0.^[33] The exponential distribution with rate \lambda > 0 features PDF f(x) = \lambda e^{-\lambda x} for x \geq 0 and 0 otherwise, modeling waiting times between events in a Poisson process.^[34]

Applications

In Probability Theory

In probability theory, probability measures provide the rigorous foundation for defining and analyzing random variables, which are essential for modeling uncertainty. A random variable X is formally defined as a measurable function from the sample space \Omega to the real numbers \mathbb{R}, meaning that for every Borel set B \subseteq \mathbb{R}, the preimage X^{-1}(B) = \{\omega \in \Omega : X(\omega) \in B\} belongs to the \sigma-algebra \mathcal{F} of the probability space (\Omega, \mathcal{F}, P).^[23] This measurability ensures that probabilities can be consistently assigned to events involving X. The induced probability measure P_X on \mathbb{R}, known as the distribution of X, is given by P_X(B) = P(X^{-1}(B)) for Borel sets B, which captures the probabilistic behavior of X without reference to the underlying space \Omega.^[23] The expectation of a random variable X, denoted E[X], quantifies its average value and is defined as the Lebesgue integral with respect to the probability measure: E[X] = \int_{\Omega} X(\omega) \, dP(\omega).^[23] For non-negative integrable random variables, this integral can be expressed using the distribution function, but in general, it requires the measure-theoretic framework to handle both discrete and continuous cases. In the discrete case, if X takes values x_i with probabilities p_i = P(X = x_i), then E[X] = \sum_i x_i p_i; in the continuous case, with density f, E[X] = \int_{-\infty}^{\infty} x f(x) \, dx, both aligning with the abstract integral definition.^[23] This unification allows expectations to serve as a fundamental tool for deriving moments and other properties. Independence is a key concept enabled by probability measures, distinguishing joint behaviors from marginal ones. Two events A, B \in \mathcal{F} are independent if P(A \cap B) = P(A) P(B), extending naturally to \sigma-algebras or random variables where the joint measure is the product of the marginal measures.^[23] For random variables X and Y, independence holds if P(X \in B_1, Y \in B_2) = P(X \in B_1) P(Y \in B_2) for all Borel sets B_1, B_2, implying that their joint distribution function factors as F_{X,Y}(x,y) = F_X(x) F_Y(x).^[23] This property underpins the analysis of systems of multiple random variables, such as in stochastic processes. Probability measures facilitate profound limit theorems that reveal asymptotic behaviors. The law of large numbers (LLN), for instance, asserts that for independent, identically distributed random variables X_1, X_2, \dots with finite expectation \mu = E[X_i], the sample average \bar{X}_n = n^{-1} \sum_{i=1}^n X_i converges almost surely to \mu as n \to \infty, a direct consequence of the measure's countable additivity and integrability conditions.^[23] Similarly, the central limit theorem (CLT) states that, under suitable moment conditions, the standardized sum ( \sum_{i=1}^n X_i - n \mu ) / \sqrt{n} \sigma converges in distribution to a standard normal random variable, where \sigma^2 = \mathrm{Var}(X_i) > 0, relying on the weak convergence of measures induced by the probability space.^[35] For example, the uniform distribution on [0,1] illustrates how such induced measures lead to normal approximations for large sums. These theorems highlight how measure-theoretic properties ensure the stability and normality of probabilistic aggregates.

In Other Fields

In statistics, the likelihood function defines densities on the parameter space relative to a suitable measure, providing a way to quantify the relative support for different parameter values given observed data. This perspective is particularly useful in infinite-dimensional settings, where the likelihood is defined relative to a dominating measure on the parameter space to ensure well-defined densities.^[36] Bayesian priors similarly function as probability measures over parameter spaces, encoding prior beliefs about model parameters before incorporating data, and enabling posterior inference through Bayes' theorem. Seminal work on such priors emphasizes their role in nonparametric settings, where they are constructed directly on spaces of probability measures to facilitate flexible modeling of uncertainty.^[37] In finance, probability measures play a central role in option pricing through the concept of risk-neutral measures, which are equivalent martingale measures that transform asset prices into martingales under a changed probability measure, allowing derivative prices to be computed as discounted expected values. This framework, foundational to modern financial mathematics, ensures no-arbitrage conditions by requiring the existence of such equivalent measures relative to the physical probability measure. The seminal Harrison-Pliska theorem establishes that a market is arbitrage-free if and only if there exists at least one equivalent martingale measure, providing the theoretical backbone for risk-neutral valuation in continuous-time models.^[38] In physics, particularly statistical mechanics, Gibbs measures describe the equilibrium distribution of systems with many interacting particles, defined via the Boltzmann factor involving the Hamiltonian energy function and inverse temperature, often as \mu(d\omega) = \frac{1}{Z} \exp(-\beta H(\omega)) \, d\omega, where Z is the partition function. These measures capture phase transitions and critical phenomena in models like the Ising model, with existence and uniqueness established under conditions on the interaction potentials. Although typically normalized to total mass 1 as probability measures, in some theoretical contexts—such as infinite-volume limits or unnormalized forms for computational purposes—Gibbs measures are treated as positive measures without explicit normalization, facilitating analysis of thermodynamic limits.^[39]^[40] In machine learning, probability measures underpin probabilistic models such as Gaussian processes, which define distributions over functions via a mean and covariance kernel, enabling Bayesian non-parametric regression and uncertainty quantification in tasks like spatial prediction and reinforcement learning. These processes are formally probability measures on function spaces, with finite-dimensional marginals being multivariate Gaussians, allowing scalable inference through kernel methods. The comprehensive treatment in Rasmussen and Williams highlights their principled probabilistic foundation, extending classical kernel regression to full Bayesian settings with priors over infinite-dimensional parameter spaces.^[41]

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[PDF] Section 18.4. The Radon-Nikodym Theorem
Feb 2, 2019 · It tells if and how it is possible to change from one probability measure to another. Specifically, the probability density function of a random ...
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14.6 - Uniform Distributions | STAT 414 - STAT ONLINE
A continuous random variable has a uniform distribution if its probability density function is defined by two constants, and the most common case is when and.
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[PDF] Normal distribution
The normal distribution is a widely used, symmetric, bell-shaped distribution, continuous from -∞ to ∞, with two parameters, µ and σ. Notation is N(µ, σ²).
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[PDF] Lecture 13 : The Exponential Distribution - UMD MATH
A continuous random variable X is said to have exponential distribution with parameter λ. If the pdf of X is (with λ > 0) f(x) = ( λe−λx , x > 0. 0 ...
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Limit Distributions For Sums Of Independent Random Variables
Dec 14, 2021 · Limit Distributions For Sums Of Independent Random Variables : B. V. Gnedenko; A. N. Kolmogorov : Free Download, Borrow, and Streaming : ...
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[PDF] On the definition of likelihood function - arXiv
Jun 21, 2021 · When we consider probability spaces and/or parameter spaces that are infinite dimensional, it is not obvious what to use as a dominating measure ...
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Prior Distributions on Spaces of Probability Measures - jstor
Methods of generating prior distributions on spaces of probability measures for use in Bayesian nonparametric inference are reviewed with special emphasis on ...
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No-Arbitrage and Equivalent Martingale Measures - SIAM.org
No-Arbitrage and Equivalent Martingale Measures: An Elementary Proof of the Harrison–Pliska Theorem ... (Risk Neutral Probability Measure With and Without Taxes).
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[PDF] Gibbs Measures and Phase Transitions on Sparse Random Graphs
Our focus here is on another area of statistical mechanics, the theory of Gibbs measures, which provides a very effective and flexible way to define ...<|separator|>
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[PDF] Gibbs Measures for Long-Range Ising Models - arXiv
Nov 5, 2019 · Gibbs measures for spin systems are probability measures defined on infinite product probability spaces of configurations of spins with values ± ...
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[PDF] Gaussian Processes for Machine Learning
Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K. I. Williams. ... probabilistic approach to learning in kernel machines ...