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97 relations: A. W. F. Edwards, Affine connection, Allen Craig, Anders Martin-Löf, Annals of Statistics, Arthur P. Dempster, Baum–Welch algorithm, Bayes' theorem, Bayesian inference, Binomial distribution, Biometrika, C (programming language), C++, C. F. Jeff Wu, Closed-form expression, Cluster analysis, Communications in Statistics, Compound probability distribution, Computer vision, Conditional probability distribution, Conjugate gradient method, Coordinate descent, David Cox (statistician), David J. C. MacKay, Debabrata Basu, Density estimation, Derivative, Donald Rubin, Entropy (information theory), Expected value, Exponential family, Gauss–Newton algorithm, George Alfred Barnard, Gibbs' inequality, Gradient descent, Graphical model, Hidden Markov model, Hill climbing, Indicator function, Information geometry, Inside–outside algorithm, Item response theory, Iterative method, Journal of the Royal Statistical Society, Kalman filter, Kullback–Leibler divergence, Latent variable, Likelihood function, Linear regression, Machine learning, ..., Marginal likelihood, Markov blanket, Matrix calculus, Maxima and minima, Maximum a posteriori estimation, Maximum likelihood estimation, Medical imaging, Message passing (disambiguation), Metaheuristic, Michael I. Jordan, Misnomer, Missing data, Mixed model, Mixture distribution, Mixture model, MM algorithm, Multimodal distribution, Multivariate normal distribution, Nan Laird, Natural language processing, Newton's method, Normal distribution, Operational Modal Analysis, Ordered subset expectation maximization, Parameter, Per Martin-Löf, Positron emission tomography, Probabilistic context-free grammar, Probability density function, Probability distribution, Psychometrics, Quantitative genetics, Random variable, Rasch model, Saddle point, Simulated annealing, Single-photon emission computed tomography, Singularity (mathematics), Statistical model, Statistics, Statistics Online Computational Resource, Structural engineering, Sufficient statistic, Total absorption spectroscopy, Variational Bayesian methods, Viterbi algorithm, Yasuo Matsuyama. Expand index (47 more) »
A. W. F. Edwards
Anthony William Fairbank Edwards, FRS (born 1935) is a British statistician, geneticist, and evolutionary biologist.
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Affine connection
In the branch of mathematics called differential geometry, an affine connection is a geometric object on a smooth manifold which connects nearby tangent spaces, so it permits tangent vector fields to be differentiated as if they were functions on the manifold with values in a fixed vector space.
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Allen Craig
Allen Thomas Craig (born July 18, 1984) is an American professional baseball first baseman and outfielder in the San Diego Padres organization.
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Anders Martin-Löf
Anders Martin-Löf (born 16 March 1940) is a Swedish physicist and mathematician.
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Annals of Statistics
The Annals of Statistics is a peer-reviewed statistics journal published by the Institute of Mathematical Statistics.
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Arthur P. Dempster
Arthur Pentland Dempster (born 1929) is a Professor Emeritus in the Harvard University Department of Statistics.
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Baum–Welch algorithm
In electrical engineering, computer science, statistical computing and bioinformatics, the Baum–Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM).
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Bayes' theorem
In probability theory and statistics, Bayes’ theorem (alternatively Bayes’ law or Bayes' rule, also written as Bayes’s theorem) describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
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Bayesian inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.
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Binomial distribution
In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability p) or failure/no/false/zero (with probability q.
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Biometrika
Biometrika is a peer-reviewed scientific journal published by Oxford University Press for the Biometrika Trust.
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C (programming language)
C (as in the letter ''c'') is a general-purpose, imperative computer programming language, supporting structured programming, lexical variable scope and recursion, while a static type system prevents many unintended operations.
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C++
C++ ("see plus plus") is a general-purpose programming language.
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C. F. Jeff Wu
Chien-Fu Jeff Wu (born 1949) is the Coca-Cola Chair in Engineering Statistics and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology.
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Closed-form expression
In mathematics, a closed-form expression is a mathematical expression that can be evaluated in a finite number of operations.
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Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).
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Communications in Statistics
Communications in Statistics is a peer-reviewed scientific journal that publishes papers related to statistics.
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Compound probability distribution
In probability and statistics, a compound probability distribution (also known as a mixture distribution or contagious distribution) is the probability distribution that results from assuming that a random variable is distributed according to some parametrized distribution, with (some of) the parameters of that distribution themselves being random variables.
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Computer vision
Computer vision is a field that deals with how computers can be made for gaining high-level understanding from digital images or videos.
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Conditional probability distribution
In probability theory and statistics, given two jointly distributed random variables X and Y, the conditional probability distribution of Y given X is the probability distribution of Y when X is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value x of X as a parameter.
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Conjugate gradient method
In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive-definite.
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Coordinate descent
Coordinate descent is an optimization algorithm that successively minimizes along coordinate directions to find the minimum of a function.
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David Cox (statistician)
Sir David Roxbee Cox (born 15 July 1924) is a prominent British statistician.
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David J. C. MacKay
Sir David John Cameron MacKay (22 April 1967 – 14 April 2016) was a British physicist, mathematician, and academic.
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Debabrata Basu
Debabrata Basu (5 July 1924 – 24 March 2001) was an Indian statistician who made fundamental contributions to the foundations of statistics.
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Density estimation
In probability and statistics, density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function.
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Derivative
The derivative of a function of a real variable measures the sensitivity to change of the function value (output value) with respect to a change in its argument (input value).
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Donald Rubin
Donald Bruce Rubin (born December 22, 1943) is the John L. Loeb Professor of Statistics at Harvard University.
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Entropy (information theory)
Information entropy is the average rate at which information is produced by a stochastic source of data.
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Expected value
In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.
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Exponential family
In probability and statistics, an exponential family is a set of probability distributions of a certain form, specified below.
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Gauss–Newton algorithm
The Gauss–Newton algorithm is used to solve non-linear least squares problems.
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George Alfred Barnard
George Alfred Barnard (23 September 1915 – 9 August 2002) was a British statistician known particularly for his work on the foundations of statistics and on quality control.
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Gibbs' inequality
Josiah Willard Gibbs In information theory, Gibbs' inequality is a statement about the mathematical entropy of a discrete probability distribution.
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Gradient descent
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
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Graphical model
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables.
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Hidden Markov model
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states.
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Hill climbing
In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search.
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Indicator function
In mathematics, an indicator function or a characteristic function is a function defined on a set X that indicates membership of an element in a subset A of X, having the value 1 for all elements of A and the value 0 for all elements of X not in A. It is usually denoted by a symbol 1 or I, sometimes in boldface or blackboard boldface, with a subscript specifying the subset.
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Information geometry
Information geometry is a branch of mathematics that applies the techniques of differential geometry to the field of probability theory.
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Inside–outside algorithm
In computer science, the inside–outside algorithm is a way of re-estimating production probabilities in a probabilistic context-free grammar.
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Item response theory
In psychometrics, item response theory (IRT) (also known as latent trait theory, strong true score theory, or modern mental test theory) is a paradigm for the design, analysis, and scoring of tests, questionnaires, and similar instruments measuring abilities, attitudes, or other variables.
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Iterative method
In computational mathematics, an iterative method is a mathematical procedure that uses an initial guess to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones.
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Journal of the Royal Statistical Society
The Journal of the Royal Statistical Society is a peer-reviewed scientific journal of statistics.
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Kalman filter
Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe.
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Kullback–Leibler divergence
In mathematical statistics, the Kullback–Leibler divergence (also called relative entropy) is a measure of how one probability distribution diverges from a second, expected probability distribution.
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Latent variable
In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured).
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Likelihood function
In frequentist inference, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model, given specific observed data.
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Linear regression
In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).
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Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
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Marginal likelihood
In statistics, a marginal likelihood function, or integrated likelihood, is a likelihood function in which some parameter variables have been marginalized.
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Markov blanket
In machine learning, the Markov blanket for a node in a graphical model contains all the variables that shield the node from the rest of the network.
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Matrix calculus
In mathematics, matrix calculus is a specialized notation for doing multivariable calculus, especially over spaces of matrices.
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Maxima and minima
In mathematical analysis, the maxima and minima (the respective plurals of maximum and minimum) of a function, known collectively as extrema (the plural of extremum), are the largest and smallest value of the function, either within a given range (the local or relative extrema) or on the entire domain of a function (the global or absolute extrema).
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Maximum a posteriori estimation
In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution.
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Maximum likelihood estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model, given observations.
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Medical imaging
Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology).
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Message passing (disambiguation)
Message passing may refer to.
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Metaheuristic
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.
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Michael I. Jordan
Michael Irwin Jordan is an American scientist, Professor at the University of California, Berkeley and a researcher in machine learning, statistics, and artificial intelligence.
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Misnomer
A misnomer is a name or term that suggests an idea that is known to be wrong.
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Missing data
In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation.
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Mixed model
A mixed model is a statistical model containing both fixed effects and random effects.
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Mixture distribution
In probability and statistics, a mixture distribution is the probability distribution of a random variable that is derived from a collection of other random variables as follows: first, a random variable is selected by chance from the collection according to given probabilities of selection, and then the value of the selected random variable is realized.
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Mixture model
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.
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MM algorithm
The MM algorithm is an iterative optimization method which exploits the convexity of a function in order to find their maxima or minima.
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Multimodal distribution
In statistics, a bimodal distribution is a continuous probability distribution with two different modes.
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Multivariate normal distribution
In probability theory and statistics, the multivariate normal distribution or multivariate Gaussian distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions.
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Nan Laird
Nan McKenzie Laird (born September 18, 1943) is a professor in Biostatistics at Harvard School of Public Health.
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Natural language processing
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
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Newton's method
In numerical analysis, Newton's method (also known as the Newton–Raphson method), named after Isaac Newton and Joseph Raphson, is a method for finding successively better approximations to the roots (or zeroes) of a real-valued function.
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Normal distribution
In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.
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Operational Modal Analysis
Ambient modal identification, also known as Operational Modal Analysis (OMA), aims at identifying the modal properties of a structure based on vibration data collected when the structure is under its operating conditions, i.e., no initial excitation or known artificial excitation.
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Ordered subset expectation maximization
In mathematical optimization, the ordered subset expectation maximization (OSEM) method is an iterative method that is used in computed tomography.
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Parameter
A parameter (from the Ancient Greek παρά, para: "beside", "subsidiary"; and μέτρον, metron: "measure"), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when identifying the system, or when evaluating its performance, status, condition, etc.
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Per Martin-Löf
Per Erik Rutger Martin-Löf (born May 8, 1942) is a Swedish logician, philosopher, and mathematical statistician.
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Positron emission tomography
Positron-emission tomography (PET) is a nuclear medicine functional imaging technique that is used to observe metabolic processes in the body as an aid to the diagnosis of disease.
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Probabilistic context-free grammar
Grammar theory to model symbol strings originated from work in computational linguistics aiming to understand the structure of natural languages.
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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.
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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.
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Psychometrics
Psychometrics is a field of study concerned with the theory and technique of psychological measurement.
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Quantitative genetics
Quantitative genetics is a branch of population genetics that deals with phenotypes that vary continuously (in characters such as height or mass)—as opposed to discretely identifiable phenotypes and gene-products (such as eye-colour, or the presence of a particular biochemical).
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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.
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Rasch model
The Rasch model, named after Georg Rasch, is a psychometric model for analyzing categorical data, such as answers to questions on a reading assessment or questionnaire responses, as a function of the trade-off between (a) the respondent's abilities, attitudes, or personality traits and (b) the item difficulty.
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Saddle point
In mathematics, a saddle point or minimax point is a point on the surface of the graph of a function where the slopes (derivatives) of orthogonal function components defining the surface become zero (a stationary point) but are not a local extremum on both axes.
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Simulated annealing
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function.
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Single-photon emission computed tomography
Single-photon emission computed tomography (SPECT, or less commonly, SPET) is a nuclear medicine tomographic imaging technique using gamma rays.
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Singularity (mathematics)
In mathematics, a singularity is in general a point at which a given mathematical object is not defined, or a point of an exceptional set where it fails to be well-behaved in some particular way, such as differentiability.
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Statistical model
A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of some sample data and similar data from a larger population.
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Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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Statistics Online Computational Resource
The Statistics Online Computational Resource (SOCR) is an online multi-institutional research and education organization.
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Structural engineering
Structural engineering is that part of civil engineering in which structural engineers are educated to create the 'bones and muscles' that create the form and shape of man made structures.
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Sufficient statistic
In statistics, a statistic is sufficient with respect to a statistical model and its associated unknown parameter if "no other statistic that can be calculated from the same sample provides any additional information as to the value of the parameter".
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Total absorption spectroscopy
Total absorption spectroscopy is a measurement technique that allows the measurement of the gamma radiation emitted in the different nuclear gamma transitions that may take place in the daughter nucleus after its unstable parent has decayed by means of the beta decay process.
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Variational Bayesian methods
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.
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Viterbi algorithm
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models.
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Yasuo Matsuyama
Yasuo Matsuyama (born March 23, 1947) is a Japanese researcher in machine learning and human-aware information processing.
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References
[1] https://en.wikipedia.org/wiki/Expectation–maximization_algorithm
