54,722 research outputs found
On Probabilistic Parallel Programs with Process Creation and Synchronisation
We initiate the study of probabilistic parallel programs with dynamic process
creation and synchronisation. To this end, we introduce probabilistic
split-join systems (pSJSs), a model for parallel programs, generalising both
probabilistic pushdown systems (a model for sequential probabilistic procedural
programs which is equivalent to recursive Markov chains) and stochastic
branching processes (a classical mathematical model with applications in
various areas such as biology, physics, and language processing). Our pSJS
model allows for a possibly recursive spawning of parallel processes; the
spawned processes can synchronise and return values. We study the basic
performance measures of pSJSs, especially the distribution and expectation of
space, work and time. Our results extend and improve previously known results
on the subsumed models. We also show how to do performance analysis in
practice, and present two case studies illustrating the modelling power of
pSJSs.Comment: This is a technical report accompanying a TACAS'11 pape
A tutorial on recursive models for analyzing and predicting path choice behavior
The problem at the heart of this tutorial consists in modeling the path
choice behavior of network users. This problem has been extensively studied in
transportation science, where it is known as the route choice problem. In this
literature, individuals' choice of paths are typically predicted using discrete
choice models. This article is a tutorial on a specific category of discrete
choice models called recursive, and it makes three main contributions: First,
for the purpose of assisting future research on route choice, we provide a
comprehensive background on the problem, linking it to different fields
including inverse optimization and inverse reinforcement learning. Second, we
formally introduce the problem and the recursive modeling idea along with an
overview of existing models, their properties and applications. Third, we
extensively analyze illustrative examples from different angles so that a
novice reader can gain intuition on the problem and the advantages provided by
recursive models in comparison to path-based ones
First-Order Decomposition Trees
Lifting attempts to speed up probabilistic inference by exploiting symmetries
in the model. Exact lifted inference methods, like their propositional
counterparts, work by recursively decomposing the model and the problem. In the
propositional case, there exist formal structures, such as decomposition trees
(dtrees), that represent such a decomposition and allow us to determine the
complexity of inference a priori. However, there is currently no equivalent
structure nor analogous complexity results for lifted inference. In this paper,
we introduce FO-dtrees, which upgrade propositional dtrees to the first-order
level. We show how these trees can characterize a lifted inference solution for
a probabilistic logical model (in terms of a sequence of lifted operations),
and make a theoretical analysis of the complexity of lifted inference in terms
of the novel notion of lifted width for the tree
PReMo : An Analyzer for P robabilistic Re cursive Mo dels
This paper describes PReMo, a tool for analyzing Recursive Markov Chains, and their controlled/game extensions: (1-exit) Recursive Markov Decision Processes and Recursive Simple Stochastic Games
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