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PyMC (formerly known as PyMC3) is a probabilistic programming language written in Python. It can be used for Bayesian statistical modeling and probabilistic machine learning. PyMC performs inference based on advanced Markov chain Monte Carlo and/or variational fitting algorithms.
MATLAB: The PDAF and JPDAF algorithms are implemented in the singleScanUpdate function that is part of the United States Naval Research Laboratory's free Tracker Component Library. [3] Python: The PDAF and other data association methods are implemented in Stone-Soup. [4] A tutorial demonstrates how the algorithms can be used. [5] [6]
The median trick is a generic approach that increases the chances of a probabilistic algorithm to succeed. [1] Apparently first used in 1986 [2] by Jerrum et al. [3] for approximate counting algorithms, the technique was later applied to a broad selection of classification and regression problems.
Probabilistic programming (PP) is a programming paradigm based on the declarative specification of probabilistic models, for which inference is performed automatically. [1] Probabilistic programming attempts to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable.
For the following definitions, two examples will be used. The first is the problem of character recognition given an array of bits encoding a binary-valued image. The other example is the problem of finding an interval that will correctly classify points within the interval as positive and the points outside of the range as negative.
Freivalds' algorithm (named after Rūsiņš Mārtiņš Freivalds) is a probabilistic randomized algorithm used to verify matrix multiplication. Given three n × n matrices A {\displaystyle A} , B {\displaystyle B} , and C {\displaystyle C} , a general problem is to verify whether A × B = C {\displaystyle A\times B=C} .
Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [19]Probabilistic numerics have also been studied for mathematical optimization, which consist of finding the minimum or maximum of some objective function given (possibly noisy or indirect) evaluations of that function at a set of points.
Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. [ 2 ] It is applicable to a variety of machine learning problems, such as collective classification , entity resolution , link prediction , and ontology alignment .