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Information Framework

Information FrameWork is an enterprise architecture framework, populated with a comprehensive set of banking specific business models. It was developed as an alternative to the Zachman Framework by Roger Evernden. The banking specific business mo ...

                                               

MODAF

The British Ministry of Defence Architecture Framework is an architecture framework which defines a standardised way of conducting enterprise architecture, originally developed by the UK Ministry of Defence. Initially the purpose of MODAF was to ...

                                               

Open Infrastructure Architecture method

The development of the method has from its early inception been published on the Internet under a Creative Commons license, using a Semantic MediaWiki based wiki for publication of standard content - originally the "DYA|Infrastructure Repository" ...

                                               

TAFIM

Technical Architecture Framework for Information Management was a 1990s reference model for enterprise architecture by and for the United States Department of Defense. TAFIM provided enterprise-level guidance for the evolution of the DoD Technica ...

                                               

TRAK

TRAK was originally commissioned by London Underground Limited. Development started in 2009 and was based on the then current views of architectural description within London Underground which were based on ISO/IEC 42010 and tied to the systems e ...

                                               

Zachman Framework

The Zachman Framework is an enterprise ontology and is a fundamental structure for Enterprise Architecture which provides a formal and structured way of viewing and defining an enterprise. The ontology is a two dimensional classification schema t ...

                                               

ALGLIB

ALGLIB is a cross-platform open source numerical analysis and data processing library. It can be used from several programming languages. ALGLIB started in 1999 and has a long history of steady development with roughly 1-3 releases per year. It i ...

                                               

AMD Core Math Library

AMD Core Math Library is an end-of-life software development library released by AMD. This library provides mathematical routines optimized for AMD processors.

                                               

Class Library for Numbers

CLN is a free library for arbitrary precision arithmetic. It operates on signed integers, rational numbers, floating point numbers, complex numbers, modular numbers, and univariate polynomials. Its implementation programming language is C++. CLN ...

                                               

IMSL Numerical Libraries

IMSL is a commercial collection of software libraries of numerical analysis functionality that are implemented in the computer programming languages C, Java, C#.NET, and Fortran. A Python interface is also available. The IMSL Libraries are provid ...

                                               

NAG Numerical Library

The NAG Numerical Library is a software product developed and sold by The Numerical Algorithms Group. It is a software library of numerical analysis routines, containing more than 1.900 mathematical and statistical algorithms. Areas covered by th ...

                                               

Trilinos

Trilinos is a collection of open-source software libraries, called packages, intended to be used as building blocks for the development of scientific applications. The word "Trilinos" is Greek and conveys the idea of "a string of pearls", suggest ...

                                               

Causal graph

In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process. They can also be viewed as a blueprint of the algorithm by ...

                                               

Factor graph

A factor graph is a bipartite graph representing the factorization of a function. In probability theory and its applications, factor graphs are used to represent factorization of a probability distribution function, enabling efficient computation ...

                                               

Graphical lasso

In statistics, the graphical lasso is a sparse penalized maximum likelihood estimator for the concentration or precision matrix of a multivariate elliptical distribution. The original variant was formulated to solve Dempsters covariance selection ...

                                               

M-separation

In statistics, m -separation is a measure of disconnectedness in ancestral graphs and a generalization of d-separation for directed acyclic graphs. It is the opposite of m -connectedness. Suppose G is an ancestral graph. For given source and targ ...

                                               

Markov random field

In the domain of physics and probability, a Markov random field, Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a M ...

                                               

Path analysis (statistics)

In statistics, path analysis is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analys ...

                                               

Latent variable model

A latent variable model is a statistical model that relates a set of observable variables to a set of latent variables. It is assumed that the responses on the indicators or manifest variables are the result of an individuals position on the late ...

                                               

Doubly stochastic model

In statistics, a doubly stochastic model is a type of model that can arise in many contexts, but in particular in modelling time-series and stochastic processes. The basic idea for a doubly stochastic model is that an observed random variable is ...

                                               

Dynamic unobserved effects model

A dynamic unobserved effects model is a statistical model used in econometrics. It is characterized by the influence of previous values of the dependent variable on its present value, and by the presence of unobservable explanatory variables. The ...

                                               

Factor regression model

The factor regression model, or hybrid factor model, is a special multivariate model with the following form: y n = A x n + B z n + c + e n {\displaystyle \mathbf {y} _{n}=\mathbf {A} \mathbf {x} _{n}+\mathbf {B} \mathbf {z} _{n}+\mathbf {c} +\ma ...

                                               

First-difference estimator

The first-difference estimator is an approach used to address the problem of omitted variables in econometrics and statistics with panel data. The estimator is obtained by running a pooled OLS estimation for a regression of Δ y i t {\displaystyle ...

                                               

Nuisance variable

In the theory of stochastic processes in probability theory and statistics, a nuisance variable is a random variable that is fundamental to the probabilistic model, but that is of no particular interest in itself or is no longer of interest: one ...

                                               

Partial least squares regression

Partial least squares regression is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression ...

                                               

Additive model

In statistics, an additive model is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle and is an essential part of the ACE algorithm. The AM uses a one-dimensional smoother to build a restricted class of ...

                                               

Errors-in-variables models

In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured ...

                                               

First-hitting-time model

Events are often triggered when a stochastic or random process first encounters a threshold. The threshold can be a barrier, boundary or specified state of a system. The amount of time required for a stochastic process, starting from some initial ...

                                               

General linear model

The general linear model or multivariate regression model is a statistical linear model. It may be written as Y = X B + U, {\displaystyle \mathbf {Y} =\mathbf {X} \mathbf {B} +\mathbf {U},} where Y is a matrix with series of multivariate measurem ...

                                               

Generalized additive model

In statistics, a generalized additive model is a generalized linear model in which the linear predictor depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions. GAMs ...

                                               

Generalized linear array model

In statistics, the generalized linear array model is used for analyzing data sets with array structures. It based on the generalized linear model with the design matrix written as a Kronecker product.

                                               

Hierarchical generalized linear model

In statistics, hierarchical generalized linear models extend generalized linear models by relaxing the assumption that error components are independent. This allows models to be built in situations where more than one error term is necessary and ...

                                               

Linear model

In statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. However, the term ...

                                               

Mixed model

A mixed model is a statistical model containing both fixed effects and random effects. These models are useful in a wide variety of disciplines in the physical, biological and social sciences. They are particularly useful in settings where repeat ...

                                               

Multilevel model

Multilevel models are statistical models of parameters that vary at more than one level. An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the student ...

                                               

Multilevel modeling for repeated measures

One application of multilevel modeling is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over time ; however, it may also be used for repeated measu ...

                                               

Multilevel regression with poststratification

Multilevel regression with poststratification is a statistical technique used for estimating preferences in sub-regions based on individual-level survey data gathered at other levels of aggregation.

                                               

Multivariate probit model

In statistics and econometrics, the multivariate probit model is a generalization of the probit model used to estimate several correlated binary outcomes jointly. For example, if it is believed that the decisions of sending at least one child to ...

                                               

Polynomial and rational function modeling

In statistical modeling, polynomial functions and rational functions are sometimes used as an empirical technique for curve fitting.

                                               

Random effects model

In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables. It is a kind of hierarchical linear model, which assumes that the data being analysed are draw ...

                                               

Regression dilution

Regression dilution, also known as regression attenuation, is the biasing of the regression slope towards zero, caused by errors in the independent variable. Consider fitting a straight line for the relationship of an outcome variable y to a pred ...

                                               

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented ...

                                               

Sinusoidal model

In statistics, signal processing, and time series analysis, a sinusoidal model to approximate a sequence Y i is: Y i = C + α sin ⁡ ω T i + ϕ + E i {\displaystyle Y_{i}=C+\alpha \sin\omega T_{i}+\phi+E_{i}} where C is constant defining a mean leve ...

                                               

Tobit model

In statistics, a tobit model is any of a class of regression models in which the observed range of the dependent variable is censored in some way. The term was coined by Arthur Goldberger in reference to James Tobin, who developed the model in 19 ...

                                               

Interdependent networks

The study of interdependent networks is a subfield of network science dealing with phenomena caused by the interactions between complex networks. Though there may be a wide variety of interactions between networks, dependency focuses on the scena ...

                                               

Markov chain central limit theorem

In the mathematical theory of random processes, the Markov chain central limit theorem has a conclusion somewhat similar in form to that of the classic central limit theorem of probability theory, but the quantity in the role taken by the varianc ...

                                               

Stochastic chains with memory of variable length

Stochastic chains with memory of variable length are a family of stochastic chains of finite order in a finite alphabet, such as, for every time pass, only one finite suffix of the past, called context, is necessary to predict the next symbol. Th ...

                                               

Candlestick chart

A candlestick chart is a style of financial chart used to describe price movements of a security, derivative, or currency. Each "candlestick" typically shows one day, thus a one-month chart may show the 20 trading days as 20 candlesticks. Candles ...

                                               

Kagi chart

The Kagi chart is a chart used for tracking price movements and to make decisions on purchasing stock. It differs from traditional stock charts such as the Candlestick chart by being mostly independent of time. This feature aids in producing a ch ...

                                               

PAC chart

Price Activity charts are a type of stock chart used in the Technical Analysis of stocks. PAC charts are unique in the way they represent "volume" the number of shares traded every day. Traditional stock charts display volume data as a histogram ...