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Numerical summaries and diagnostics for Markov chain Monte Carlo methods. Integration with established probabilistic programming languages including; PyStan (the Python interface of Stan ), PyMC , [ 15 ] Edward [ 16 ] Pyro, [ 17 ] and easily integrated with novel or bespoke Bayesian analyses.
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.
C++03 is a version of the ISO/IEC 14882 standard for the C++ programming language. It is defined by two standards organizations, the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), in standard ISO/IEC 14882:2003. C++03 replaced the prior C++98 standard. C++03 was later replaced by C++11.
Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. [14] A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, [15] Uber's Pyro, [16] Hugging Face's Transformers, [17] PyTorch Lightning, [18] [19] and Catalyst. [20] [21]
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.
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Differentiable programming has been applied in areas such as combining deep learning with physics engines in robotics, [12] solving electronic structure problems with differentiable density functional theory, [13] differentiable ray tracing, [14] image processing, [15] and probabilistic programming. [5]