As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages that treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive power. In this paper, we present a probabilistic language, called λο, which uniformly supports all kinds of probability distributions -- discrete distributions, continuous distributions, and even those belonging to neither group. Its mathematical basis is sampling functions, i.e., mappings from the unit interval (0.0,1.0] to probability domains.We also briefly describe the implementation of λο as an extension of Objective CAML and demonstrate its practicality with three applications in robotics: robot localization, people tracking, and robotic mapping. All experiments have been carried out with real robots.
A probabilistic language based upon sampling functions
Marc Berndl,Ondřej Lhoták,Feng Qian,L. Hendren,Navindra Umanee
Published 2005 in ACM-SIGACT Symposium on Principles of Programming Languages
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- Publication year
2005
- Venue
ACM-SIGACT Symposium on Principles of Programming Languages
- Publication date
2005-01-12
- Fields of study
Mathematics, Computer Science
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