Ambiguous Probabilistic Programs
نویسندگان
چکیده
Probabilistic programs are widely used decision models. When implemented in practice, however, there often exists distributional ambiguity in these models. In this paper, we model the ambiguity using the likelihood ratio (LR) and use LR to construct various ambiguity sets. We consider ambiguous probabilistic programs which optimize under the worst case. Ambiguous probabilistic programs can be classified as ambiguous probability minimization problems (PM) and ambiguous chance constrained programs (CCP). We show that the ambiguous PM can be transformed to a pure PM under the nominal distribution, and that the ambiguous CCP can be transformed to a pure CCP with only the confidence level being rescaled from the original CCP. Our study indicates that ambiguous probabilistic programs with ambiguity modeled by LR essentially have the same complexity as the corresponding pure probabilistic programs and that risk and uncertainty have strong connections in probabilistic programs.
منابع مشابه
Integrating Probabilistic Modeling and Representation-Building
Optimization algorithms are adaptive when they sample problem solutions based on knowledge of the overall search space gathered from past sampling. Recently, competent adaptive optimization algorithms have been developed that achieve this adaptability via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms o...
متن کاملCHR(PRISM)-based probabilistic logic learning
PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level programming language based on multi-headed multiset rewrite rules. In this paper, we introduce a new probabilistic logic formalism, called CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level rapid pro...
متن کاملProbabilistic Automata of Bounded Ambiguity
Probabilistic automata are a computational model introduced by Michael Rabin, extending nondeterministic finite automata with probabilistic transitions. Despite its simplicity, this model is very expressive and many of the associated algorithmic questions are undecidable. In this work we focus on the emptiness problem, which asks whether a given probabilistic automaton accepts some word with pr...
متن کاملMavris, DeLaurentis ICAS 145.1 Methodology for Examining the Simultaneous Impact of Requirements, Vehicle Characteristics, and Technologies on Military Aircraft Design
The process of system engineering has always emphasized the definition of requirements as the first step toward product development. Typically, however, these requirements were examined in isolation from the potential systems and technologies they would likely impact. Further, requirements during design were treated deterministically, which sometimes led to nonrobust and poor performing actual ...
متن کاملZooming in on Trade-Offs in Qualitative Probabilistic Networks
Qualitative probabilistic networks have been designed for probabilistic reasoning in a qualitative way. As a consequence of their coarse level of representation detail, qualitative probabilistic networks do not provide for resolving trade-offs and typically yield ambiguous results upon inference. We present an algorithm for computing more informative results for unresolved trade-offs. The algor...
متن کامل