Semantics of Probabilistic Programs

Two complementary but equivalent semantic interpretations of a high level probabilistic programming language are given. One of these interprets programs as partial measurable functions on a measurable space. The other interprets programs as continuous linear operators on a Banach space of measures. It is shown how the ordered domains of Scott and others are embedded naturally into these spaces. Two general results about probabilistic programs are proved.

Authors

• Dexter Kozen

Keywords

• Extraterrestrial measurements
• Computer languages
• Power system modeling
• Surges
• Power generation economics
• Combinatorial mathematics
• Algorithm design and analysis
• Stochastic processes
• Decision trees
• Turing machines
• Probabilistic Programming
• Part Of Function
• Banach Space
• Measure Space
• Random Variables
• Random Number
• Fixed Point
• Random Generation
• Random Assignment
• Set Of Elements
• Continuous Distribution
• Point-like
• Random Vector
• Discrete Distribution
• Probability Theory
• Unit Sphere
• Maximum Norm
• Discrete Measurements
• Discrete Parts
• Positive Cone
• Top Element
• Program Behavior
• Isotonicity
• Class Program
• Random Walk