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A probabilistic language based upon sampling functions
 In Conference Record of the 32nd Annual ACM Symposium on Principles of Programming Languages
, 2005
"... As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive p ..."
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Cited by 33 (0 self)
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As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive power. This paper presents a probabilistic language, called λ○, whose expressive power is beyond discrete distributions. Rich expressiveness of λ ○ is due to its use of sampling functions, i.e., mappings from the unit interval (0.0, 1.0] to probability domains, in specifying probability distributions. As such, λ ○ enables programmers to formally express and reason about sampling methods developed in simulation theory. The use of λ ○ is demonstrated with three applications in robotics: robot localization, people tracking, and robotic mapping. All experiments have been carried out with real robots.
The design and implementation of IBAL: A generalpurpose probabilistic programming language
 Harvard Univesity
, 2005
"... In a rational programming language, a program specifes a situation encountered by an agent; evaluating the program amounts to computing what a rational agent would believe or do in the situation. Rational programming combines the advantages of declarative representations with features of programming ..."
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Cited by 21 (1 self)
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In a rational programming language, a program specifes a situation encountered by an agent; evaluating the program amounts to computing what a rational agent would believe or do in the situation. Rational programming combines the advantages of declarative representations with features of programming languages such
A probabilistic language based on sampling functions
 ACM Transactions on Programming Languages and Systems
, 2006
"... As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive p ..."
Abstract

Cited by 16 (0 self)
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As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive power. This article presents a probabilistic language, called λ○, whose expressive power is beyond discrete distributions. Rich expressiveness of λ ○ is due to its use of sampling functions, that is, mappings from the unit interval (0.0, 1.0] to probability domains, in specifying probability distributions. As such, λ ○ enables programmers to formally express and reason about sampling methods developed in simulation theory. The use of λ ○ is demonstrated with three applications in robotics: robot localization, people tracking, and robotic mapping. All experiments have been carried out with real robots.
A Monadic Probabilistic Language
 In Proceedings of the 2003 ACM SIGPLAN international workshop on Types in languages design and implementation
, 2003
"... Motivated by many practical applications that have to compute in the presence of uncertainty, we propose a monadic probabilistic language based upon the mathematical notion of sampling function. Our language provides a unified representation scheme for probability distributions, enjoys rich expressi ..."
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Cited by 10 (5 self)
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Motivated by many practical applications that have to compute in the presence of uncertainty, we propose a monadic probabilistic language based upon the mathematical notion of sampling function. Our language provides a unified representation scheme for probability distributions, enjoys rich expressiveness, and o#ers high versatility in encoding probability distributions. We also develop a novel style of operational semantics called a horizontal operational semantics, under which an evaluation returns not a single outcome but multiple outcomes. We have preliminary evidence that the horizontal operational semantics improves the ordinary operational semantics with respect to both execution time and accuracy in representing probability distributions.
A FirstOrder Stochastic Modeling Language for Diagnosis
 Proceedings of the Eighteenth International FLAIRS Conference. (AAAI
, 2005
"... We have created a logicbased, firstorder, and Turingcomplete set of software tools for stochastic modeling. Because the inference scheme for this language is based on a variant of Pearl's loopy belief propagation algorithm, we call it Loopy Logic. Traditional Bayesian belief networks have lim ..."
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Cited by 4 (2 self)
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We have created a logicbased, firstorder, and Turingcomplete set of software tools for stochastic modeling. Because the inference scheme for this language is based on a variant of Pearl's loopy belief propagation algorithm, we call it Loopy Logic. Traditional Bayesian belief networks have limited expressive power, basically constrained to that of atomic elements as in the propositional calculus. Our language contains variables that can capture general classes of situations, events, and relationships. A Turingcomplete language is able to reason about potentially infinite classes and situations, with a Dynamic Bayesian Network. Since the inference algorithm for Loopy Logic is based on a variant of loopy belief propagation, the language includes an Expectation Maximizationtype learning of parameters in the modeling domain. In this paper we briefly present the theoretical foundations for our loopylogic language and then demonstrate several examples of stochastic modeling and diagnosis.
A Programming Language for Probabilistic Computation
, 2005
"... As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages to facilitate their modeling. Most of the existing probabilistic languages, however, focus only on discrete distributions, and there has been little effort to develop ..."
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Cited by 2 (0 self)
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As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages to facilitate their modeling. Most of the existing probabilistic languages, however, focus only on discrete distributions, and there has been little effort to develop probabilistic languages whose expressive power is beyond discrete distributions. This dissertation presents a probabilistic language, called PTP (ProbabilisTic Programming), which supports all kinds of probability distributions.
A LogicBased FirstOrder Stochastic Language that Learns
"... We have created a logicbased, firstorder, and Turingcomplete set of software tools for stochastic modeling. Because the inference scheme for this language is based on a variant of Pearl’s loopy belief propagation algorithm, we call it Loopy Logic. Traditional Bayesian belief networks have limited ..."
Abstract
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We have created a logicbased, firstorder, and Turingcomplete set of software tools for stochastic modeling. Because the inference scheme for this language is based on a variant of Pearl’s loopy belief propagation algorithm, we call it Loopy Logic. Traditional Bayesian belief networks have limited expressive power, basically constrained to that of atomic elements as in the propositional calculus. A firstorder language contains variables that can capture general classes of situations, events, and relationships. A Turingcomplete language is able to reason about potentially infinite classes and situations. Since the inference algorithm for Loopy Logic is based on a variant of loopy belief propagation, the language includes an Expectation Maximizationtype learning of parameters in the modeling domain. In this paper we present the theoretical foundations for our loopylogic language and then demonstrate several examples using the Loopy Logic system for stochastic modeling and diagnosis. 1
A FirstOrder Stochastic Prognostic System for the Diagnosis of Helicopter Rotor Systems for the US Navy
"... Abstract C We have created a diagnostic system for the US Navy to use in the analysis of the “running health ” of helicopter rotor systems. Although our system is not yet deployed for realtime inflight diagnosis, we have successfully analyzed the data sets of actual helicopter rotor failures suppl ..."
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Abstract C We have created a diagnostic system for the US Navy to use in the analysis of the “running health ” of helicopter rotor systems. Although our system is not yet deployed for realtime inflight diagnosis, we have successfully analyzed the data sets of actual helicopter rotor failures supplied by the US Navy. We discuss both critical techniques supporting the design of our stochastic diagnostic system as well as issues related to full deployment. Our diagnostic system, called DBAYES, is composed of a logicbased, firstorder, and Turingcomplete set of software tools for stochastic modeling. We use this language for modeling timeseries data supplied by sensors on the mechanical system. The inference scheme for these software tools is based on a variant of Pearl's loopy belief propagation algorithm. Our language contains variables that can capture general classes of situations, events, and relationships. A Turingcomplete language is able to reason about potentially infinite classes and situations, similar to the analysis of Dynamic Bayesian Networks. Since the inference algorithm is based on a variant of loopy belief propagation, the language includes the Expectation Maximization type learning of parameters in the modeled domain. In this paper we briefly present the theoretical foundations for our firstorder stochastic language and then demonstrate timeseries modeling and learning in the context of fault diagnosis. 1.