Efficient Probabilistic Programming Languages
نویسنده
چکیده
In recent years, declarative programming languages specialized for probabilistic modeling has emerged as distinct class of languages. These languages are predominantly written by researchers in the machine learning field and concentrate on generalized MCMC inference algorithm. Unfortunately, all these languages are too slow for practical adoption. In my talk, I will outline several places where compiler optimizations could improve these languages and make them more usable in an industrial setting.
منابع مشابه
Probabilistic Programming : Concepts and Challenges ( Extended Abstract ) Angelika Kimmig and Luc
A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of and reasoning with complex, structured probability distributions. Examples include functional languages (Church [Goodman et al., 2008], IBAL [Pfeffer, 2001]), object-oriented languages (Figaro [Pfeffer, 2009]), and logic languages (Pr...
متن کاملNonstandard Interpretations of Probabilistic Programs for Efficient Inference
Probabilistic programming languages allow modelers to specify a stochastic process using syntax that resembles modern programming languages. Because the program is in machine-readable format, a variety of techniques from compiler design and program analysis can be used to examine the structure of the distribution represented by the probabilistic program. We show how nonstandard interpretations ...
متن کاملA Fuzzy Goal Programming Model for Efficient Portfolio Selection.
This paper considers a multi-objective portfolio selection problem imposed by gaining of portfolio, divided yield and risk control in an ambiguous investment environment, in which the return and risk are characterized by probabilistic numbers. Based on the theory of possibility, a new multi-objective portfolio optimization model with gaining of portfolio, divided yield and risk control is propo...
متن کاملA Step from Probabilistic Programming to Cognitive Architectures
Probabilistic programming is considered as a framework, in which basic components of cognitive architectures can be represented in unified and elegant fashion. At the same time, necessity of adopting some component of cognitive architectures for extending capabilities of probabilistic programming languages is pointed out. In particular, implicit specification of generative models via declaratio...
متن کاملA Compilation Target for Probabilistic Programming Languages
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermedi...
متن کامل