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A probabilistic programming language (PPL) is a programming language designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.〔 〕 Probabilistic programming represents an attempt to "() general purpose programming with probabilistic modeling."〔(【引用サイトリンク】title=Probabilistic Programming )〕 Probabilistic reasoning is a foundational technology of machine learning. It is used by companies such as Google, Amazon.com and Microsoft. Probabilistic reasoning has been used for predicting stock prices, recommending movies, diagnosing computers, detecting cyber intrusions and image detection.〔 PPLs often extend from a basic language. The choice of underlying basic language depends on the similarity of the model to the basic language's ontology, as well as commercial considerations and personal preference. For instance, Dimple〔 and Chimple〔 are based on Java, Infer.NET is based on .NET framework,〔 while PRISM extends from Prolog.〔 However, some PPLs such as WinBUGS and Stan offer a self-contained language, with no obvious origin in another language.〔〔 Several PPLs are in active development, including some in beta test. == Relational == A probabilistic relational programming language (PRPL) is a PPL specially designed to describe and infer with probabilistic relational models (PRMs). A PRM is usually developed with a set of algorithms for reducing, inference about and discovery of concerned distributions, which are embedded into the corresponding PRPL. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「probabilistic programming language」の詳細全文を読む スポンサード リンク
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