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Stan (software), the Glossary

Stan is a probabilistic programming language for statistical inference written in C++.^[1]

40 relations: Andrew Gelman, ArviZ, Automatic differentiation, Bobby Carpenter, Broyden–Fletcher–Goldfarb–Shanno algorithm, C++, Daniel Lee, Forecasting, Git, GitHub, Hamiltonian Monte Carlo, IA-32, Imperative programming, Journal of Machine Learning Research, Laplace's approximation, Limited-memory BFGS, MacOS, Market research, Markov chain Monte Carlo, Mathematical optimization, MATLAB, Medical imaging, Medical statistics, Microsoft Windows, Monte Carlo method, Probabilistic programming, Probability density function, PyMC, Python (programming language), R (programming language), Ruby, Stanisław Ulam, Stata, Statistical inference, Statistical model, Time series, Unix shell, Unix-like, Variational Bayesian methods, X86-64.
Free Bayesian statistics software
Monte Carlo software
Probabilistic software

Andrew Gelman

Andrew Eric Gelman (born February 11, 1965) is an American statistician and professor of statistics and political science at Columbia University.

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ArviZ is a Python package for exploratory analysis of Bayesian models. Stan (software) and ArviZ are computational statistics, Free Bayesian statistics software, Monte Carlo software, numerical programming languages and probabilistic software.

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Automatic differentiation

In mathematics and computer algebra, automatic differentiation (auto-differentiation, autodiff, or AD), also called algorithmic differentiation, computational differentiation, is a set of techniques to evaluate the partial derivative of a function specified by a computer program.

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Bobby Carpenter

Bobby or Bob Carpenter may refer to.

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Broyden–Fletcher–Goldfarb–Shanno algorithm

In numerical optimization, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems.

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C++

C++ (pronounced "C plus plus" and sometimes abbreviated as CPP) is a high-level, general-purpose programming language created by Danish computer scientist Bjarne Stroustrup.

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Daniel Lee

Daniel Lee may refer to.

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Forecasting

Forecasting is the process of making predictions based on past and present data.

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Git

Git is a distributed version control system that tracks versions of files.

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GitHub

GitHub is a developer platform that allows developers to create, store, manage and share their code.

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Hamiltonian Monte Carlo

The Hamiltonian Monte Carlo algorithm (originally known as hybrid Monte Carlo) is a Markov chain Monte Carlo method for obtaining a sequence of random samples which converge to being distributed according to a target probability distribution for which direct sampling is difficult.

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IA-32

IA-32 (short for "Intel Architecture, 32-bit", commonly called i386) is the 32-bit version of the x86 instruction set architecture, designed by Intel and first implemented in the 80386 microprocessor in 1985.

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Imperative programming

In computer science, imperative programming is a programming paradigm of software that uses statements that change a program's state.

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Journal of Machine Learning Research

The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning.

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Laplace's approximation

Laplace's approximation provides an analytical expression for a posterior probability distribution by fitting a Gaussian distribution with a mean equal to the MAP solution and precision equal to the observed Fisher information.

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Limited-memory BFGS

Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory.

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MacOS

macOS, originally Mac OS X, previously shortened as OS X, is an operating system developed and marketed by Apple since 2001.

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Market research

Market research is an organized effort to gather information about target markets and customers.It involves understanding who they are and what they need.

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Markov chain Monte Carlo

In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Stan (software) and Markov chain Monte Carlo are computational statistics.

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Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives.

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MATLAB

MATLAB (an abbreviation of "MATrix LABoratory") is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. Stan (software) and MATLAB are Domain-specific programming languages and numerical programming languages.

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Medical imaging

Medical imaging is the technique and process of imaging 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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Medical statistics

Medical statistics (also health statistics) deals with applications of statistics to medicine and the health sciences, including epidemiology, public health, forensic medicine, and clinical research.

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Microsoft Windows

Microsoft Windows is a product line of proprietary graphical operating systems developed and marketed by Microsoft.

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Monte Carlo method

Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results.

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Probabilistic programming

Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. Stan (software) and probabilistic programming are probabilistic software.

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Probability density function

In probability theory, a probability density function (PDF), density function, or density of an absolutely 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 be equal to that sample.

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PyMC

PyMC (formerly known as PyMC3) is a probabilistic programming language written in Python. Stan (software) and PyMC are computational statistics, Free Bayesian statistics software, Monte Carlo software, numerical programming languages and probabilistic software.

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References

[1] https://en.wikipedia.org/wiki/Stan_(software)

Stan (software), the Glossary

Table of Contents

See also

Free Bayesian statistics software

Monte Carlo software

Probabilistic software

References