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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 26 (1 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.
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 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 9 (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.
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 9 (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 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 limited ..."
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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 1 (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