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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.
The probabilistic logic programming language P-Log resolves this by dividing the probability mass equally between the answer sets, following the principle of indifference. [4] [6] Alternatively, probabilistic answer set programming under the credal semantics allocates a credal set to every query. Its lower probability bound is defined by only ...
Stan is a probabilistic programming language for statistical inference written in C++. [2] The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function. [2] Stan is licensed under the New BSD License.
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.
OpenNN – A software library written in the programming language C++ which implements neural networks, a main area of deep learning research; Orange, a data mining, machine learning, and bioinformatics software; Pandas – High-performance computing (HPC) data structures and data analysis tools for Python in Python and Cython (statsmodels ...
The main article for this category is Probabilistic programming language. Pages in category "Probabilistic software" The following 16 pages are in this category, out of 16 total.
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 .
A probabilistic logic network (PLN) is a conceptual, mathematical and computational approach to uncertain inference. It was inspired by logic programming and it uses probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values .