Modular Runtime Complexity Analysis of Probabilistic While Programs

08/23/2019 ∙ by Martin Avanzini, et al. ∙ Leopold Franzens Universität Innsbruck Inria 0

We are concerned with the average case runtime complexity analysis of a prototypical imperative language endowed with primitives for sampling and probabilistic choice. Taking inspiration from known approaches from to the modular resource analysis of non-probabilistic programs, we investigate how a modular runtime analysis is obtained for probabilistic programs.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Preliminaries

Multisets

A (possibly infinite) multiset over a set is a mapping . The union of a countably many multisets is defined by

which forms a multiset if and only if is finite for every . The sum of a multiset with respect to is defined by

We use set-like notations for multisets: denotes the empty multiset , is the multiset with , and is its special case where is finite.

Weighted Abstract Reduction Systems

A weighted ARS over state space is a ternary relation . We write meaning . We define the ARS induced by at cost , by the following inference rules:

We say is a normal form with respect to if there exists no with . The set of normal forms with respect to is denoted by . The potential of with respect to is defined by . The weighted ARS is called strongly bounded on , , if for every , there exists such that implies ; This is equivalent to saying that for every .

Weighted Probabilistic Abstract Reduction Systems

A multidistribution on a set is a multiset of pairs of and , written , satisfying . We denote the set of multidistributions on by . Multidistributions are closed under convex multiset unions for every finite or countable infinite index set

and probabilities

with , where scalar multiplication is defined by for .

The restriction of a multidistribution to a set is defined by . For a function , we denote by its homomorphic extension defined by

For , we define the expectation of a function as . Notice that .

Definition 1.

A weighted probabilistic ARS over is a set . As before, we may write for . We define the weighted ARS over induced by as follows:

For a weighted probabilistic ARS , let us define . A weighted probabilistic ARS over is strongly bounded on a set if , i.e., for all .

2 Probabilistic While

We consider an imperative language in the spirit of Dijkstra’s Guarded Command Language [Dijkstra75], endowed with primitives for sampling from discrete distributions as well as non-deterministic and probabilistic choice. Let denote a finite set of integer-valued variables . We denote by the set of stores, that associate variables with their integer contents. The syntax of program commands over is given by the following grammar.

effectless operation
resource consumption
termination
probabilistic assignment