69,847 research outputs found
A New Proof Rule for Almost-Sure Termination
An important question for a probabilistic program is whether the probability
mass of all its diverging runs is zero, that is that it terminates "almost
surely". Proving that can be hard, and this paper presents a new method for
doing so; it is expressed in a program logic, and so applies directly to source
code. The programs may contain both probabilistic- and demonic choice, and the
probabilistic choices may depend on the current state.
As do other researchers, we use variant functions (a.k.a.
"super-martingales") that are real-valued and probabilistically might decrease
on each loop iteration; but our key innovation is that the amount as well as
the probability of the decrease are parametric.
We prove the soundness of the new rule, indicate where its applicability goes
beyond existing rules, and explain its connection to classical results on
denumerable (non-demonic) Markov chains.Comment: V1 to appear in PoPL18. This version collects some existing text into
new example subsection 5.5 and adds a new example 5.6 and makes further
remarks about uncountable branching. The new example 5.6 relates to work on
lexicographic termination methods, also to appear in PoPL18 [Agrawal et al,
2018
On the Termination Problem for Probabilistic Higher-Order Recursive Programs
In the last two decades, there has been much progress on model checking of
both probabilistic systems and higher-order programs. In spite of the emergence
of higher-order probabilistic programming languages, not much has been done to
combine those two approaches. In this paper, we initiate a study on the
probabilistic higher-order model checking problem, by giving some first
theoretical and experimental results. As a first step towards our goal, we
introduce PHORS, a probabilistic extension of higher-order recursion schemes
(HORS), as a model of probabilistic higher-order programs. The model of PHORS
may alternatively be viewed as a higher-order extension of recursive Markov
chains. We then investigate the probabilistic termination problem -- or,
equivalently, the probabilistic reachability problem. We prove that almost sure
termination of order-2 PHORS is undecidable. We also provide a fixpoint
characterization of the termination probability of PHORS, and develop a sound
(but possibly incomplete) procedure for approximately computing the termination
probability. We have implemented the procedure for order-2 PHORSs, and
confirmed that the procedure works well through preliminary experiments that
are reported at the end of the article
Exact Methods for Multistage Estimation of a Binomial Proportion
We first review existing sequential methods for estimating a binomial
proportion. Afterward, we propose a new family of group sequential sampling
schemes for estimating a binomial proportion with prescribed margin of error
and confidence level. In particular, we establish the uniform controllability
of coverage probability and the asymptotic optimality for such a family of
sampling schemes. Our theoretical results establish the possibility that the
parameters of this family of sampling schemes can be determined so that the
prescribed level of confidence is guaranteed with little waste of samples.
Analytic bounds for the cumulative distribution functions and expectations of
sample numbers are derived. Moreover, we discuss the inherent connection of
various sampling schemes. Numerical issues are addressed for improving the
accuracy and efficiency of computation. Computational experiments are conducted
for comparing sampling schemes. Illustrative examples are given for
applications in clinical trials.Comment: 38 pages, 9 figure
How long, O Bayesian network, will I sample thee? A program analysis perspective on expected sampling times
Bayesian networks (BNs) are probabilistic graphical models for describing
complex joint probability distributions. The main problem for BNs is inference:
Determine the probability of an event given observed evidence. Since exact
inference is often infeasible for large BNs, popular approximate inference
methods rely on sampling.
We study the problem of determining the expected time to obtain a single
valid sample from a BN. To this end, we translate the BN together with
observations into a probabilistic program. We provide proof rules that yield
the exact expected runtime of this program in a fully automated fashion. We
implemented our approach and successfully analyzed various real-world BNs taken
from the Bayesian network repository
Inductive types in the Calculus of Algebraic Constructions
In a previous work, we proved that an important part of the Calculus of
Inductive Constructions (CIC), the basis of the Coq proof assistant, can be
seen as a Calculus of Algebraic Constructions (CAC), an extension of the
Calculus of Constructions with functions and predicates defined by higher-order
rewrite rules. In this paper, we prove that almost all CIC can be seen as a
CAC, and that it can be further extended with non-strictly positive types and
inductive-recursive types together with non-free constructors and
pattern-matching on defined symbols.Comment: Journal version of TLCA'0
Approximating the Termination Value of One-Counter MDPs and Stochastic Games
One-counter MDPs (OC-MDPs) and one-counter simple stochastic games (OC-SSGs)
are 1-player, and 2-player turn-based zero-sum, stochastic games played on the
transition graph of classic one-counter automata (equivalently, pushdown
automata with a 1-letter stack alphabet). A key objective for the analysis and
verification of these games is the termination objective, where the players aim
to maximize (minimize, respectively) the probability of hitting counter value
0, starting at a given control state and given counter value. Recently, we
studied qualitative decision problems ("is the optimal termination value = 1?")
for OC-MDPs (and OC-SSGs) and showed them to be decidable in P-time (in NP and
coNP, respectively). However, quantitative decision and approximation problems
("is the optimal termination value ? p", or "approximate the termination value
within epsilon") are far more challenging. This is so in part because optimal
strategies may not exist, and because even when they do exist they can have a
highly non-trivial structure. It thus remained open even whether any of these
quantitative termination problems are computable. In this paper we show that
all quantitative approximation problems for the termination value for OC-MDPs
and OC-SSGs are computable. Specifically, given a OC-SSG, and given epsilon >
0, we can compute a value v that approximates the value of the OC-SSG
termination game within additive error epsilon, and furthermore we can compute
epsilon-optimal strategies for both players in the game. A key ingredient in
our proofs is a subtle martingale, derived from solving certain LPs that we can
associate with a maximizing OC-MDP. An application of Azuma's inequality on
these martingales yields a computable bound for the "wealth" at which a "rich
person's strategy" becomes epsilon-optimal for OC-MDPs.Comment: 35 pages, 1 figure, full version of a paper presented at ICALP 2011,
invited for submission to Information and Computatio
Breaking Up a Research Consortium
Inter-firm R&D collaborations through contractual arrangements have become increasingly
popular, but in many cases they are broken up without any joint discovery.
We provide a rationale for the breakup date in R&D collaboration agreements. More
specifically, we consider a research consortium initiated by a firm A with a firm B. B has
private information about whether it is committed to the project or a free-rider. We
show that under fairly general conditions, a breakup date in the contract is a (secondbest)
optimal screening device for firm A to screen out free-riders. With the additional
constraint of renegotiation proofness, A can only partially screen out free-riders: entry
by some free-riders makes sure that A does not have an incentive to renegotiate the
contract ex post. We also propose empirical strategies for identifying the three likely
causes of a breakup date: adverse selection, moral hazard, and project non-viability
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