CTPPL: A Continuous Time Probabilistic Programming Language
نویسنده
چکیده
Probabilistic programming languages allow a modeler to build probabilistic models using complex data structures with all the power of a programming language. We present CTPPL, an expressive probabilistic programming language for dynamic processes that models processes using continuous time. Time is a first class element in our language; the amount of time taken by a subprocess can be specified using the full power of the language. We show through examples that CTPPL can easily represent existing continuous time frameworks and makes it easy to represent new ones. We present semantics for CTPPL in terms of a probability measure over trajectories. We present a particle filtering algorithm for the language that works for a large and useful class of CTPPL programs.
منابع مشابه
Inference for a New Probabilistic Constraint Logic
Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. In this paper, we propose a new probabilistic constraint logic programming language, which combines constraint logic programming with probabilistic reasoning. The language supports mode...
متن کاملUsing Probabilistic-Risky Programming Models in Identifying Optimized Pattern of Cultivation under Risk Conditions (Case Study: Shoshtar Region)
Using Telser and Kataoka models of probabilistic-risky mathematical programming, the present research is to determine the optimized pattern of cultivating the agricultural products of Shoshtar region under risky conditions. In order to consider the risk in the mentioned models, time period of agricultural years 1996-1997 till 2004-2005 was taken into account. Results from Telser and Kataoka mod...
متن کاملOn Continuous Distributions and Parameter Estima- tion in Probabilistic Logic Programs
In the last decade remarkable progress has been made on combining statistical machine learning techniques, reasoning under uncertainty, and relational representations. The branch of Artificial Intelligence working on the synthesis of these three areas is known as statistical relational learning or probabilistic logic learning. ProbLog, one of the probabilistic frameworks developed, is an extens...
متن کاملABC-Fun: A Probabilistic Programming Language for Biology
Formal methods have long been employed to capture the dynamics of biological systems in terms of Continuous Time Markov Chains. The formal approach enables the use of elegant analysis tools such as model checking, but usually relies on a complete specification of the model of interest and cannot easily accommodate uncertain data. In contrast, data-driven modelling, based on machine learning tec...
متن کاملPseudoconvex Multiobjective Continuous-time Problems and Vector Variational Inequalities
In this paper, the concept of pseudoconvexity and quasiconvexity for continuous~-time functions are studied and an equivalence condition for pseudoconvexity is obtained. Moreover, under pseudoconvexity assumptions, some relationships between Minty and Stampacchia vector variational inequalities and continuous-time programming problems are presented. Finally, some characterizations of the soluti...
متن کامل