Benjamin Lucien Kaminski

Weakest Precondition Reasoning for Expected Run---Times of Probabilistic Programs

This paper presents a wp---style calculus for obtaining bounds on the expected run---time of probabilistic programs. Its application includes determining the possibly infinite expected termination time of a probabilistic program and proving positive almost---sure termination--does a program terminate with probability one in finite expected time? We provide several proof rules for bounding the run---time of loops, and prove the soundness of the approach with respect to a simple operational model. We show that our approach is a conservative extension of Nielsons approach for reasoning about the run---time of deterministic programs. We analyze the expected run---time of some example programs including a one---dimensional random walk and the coupon collector problem.

On the Hardness of Almost–Sure Termination

This paper considers the computational hardness of computing expected outcomes and deciding (universal) (positive) almost–sure termination of probabilistic programs. It is shown that computing lower and upper bounds of expected outcomes is \(\varSigma _1^0\)– and \(\varSigma _2^0\)–complete, respectively. Deciding (universal) almost–sure termination as well as deciding whether the expected outcome of a program equals a given rational value is shown to be \(\varPi ^0_2\)–complete. Finally, it is shown that deciding (universal) positive almost–sure termination is \(\varSigma _2^0\)–complete (\(\varPi _3^0\)–complete).

Reasoning about Recursive Probabilistic Programs

This paper presents a wp–style calculus for obtaining expectations on the outcomes of (mutually) recursive probabilistic programs. We provide several proof rules to derive one– and two–sided bounds for such expectations, and show the soundness of our wp–calculus with respect to a probabilistic pushdown automaton semantics. We also give a wp–style calculus for obtaining bounds on the expected runtime of recursive programs that can be used to determine the (possibly infinite) time until termination of such programs.

Understanding Probabilistic Programs

We present two views of probabilistic programs and their relationship. An operational interpretation as well as a weakest pre-condition semantics are provided for an elementary probabilistic guarded command language. Our study treats important features such as sampling, conditioning, loop divergence, and non-determinism.

A weakest pre-expectation semantics for mixed-sign expectations

We present a weakest-precondition-style calculus for reasoning about the expected values (pre-expectations) of mixed-sign unbounded random variables after execution of a probabilistic program. The semantics of a while-loop is defined as the limit of iteratively applying a functional to a zero-element just as in the traditional weakest pre-expectation calculus, even though a standard least fixed point argument is not applicable in our semantics. A striking feature of our semantics is that it is always well-defined, even if the expected values do not exist. We show that the calculus is sound and allows for compositional reasoning. Furthermore, we present an invariant-based approach for reasoning about pre-expectations of loops.

Inferring Covariances for Probabilistic Programs

We study weakest precondition reasoning about the (co)variance of outcomes and the variance of run–times of probabilistic programs with conditioning. For outcomes, we show that approximating (co)variances is computationally more difficult than approximating expected values. In particular, we prove that computing both lower and upper bounds for (co)variances is \(\varSigma _2^0\)–complete. As a consequence, neither lower nor upper bounds are computably enumerable. We therefore present invariant–based techniques that do enable enumeration of both upper and lower bounds, once appropriate invariants are found. Finally, we extend this approach to reasoning about run–time variances.

Bounded Model Checking for Probabilistic Programs

In this paper we investigate the applicability of standard model checking approaches to verifying properties in probabilistic programming. As the operational model for a standard probabilistic program is a potentially infinite parametric Markov decision process, no direct adaption of existing techniques is possible. Therefore, we propose an on–the–fly approach where the operational model is successively created and verified via a step–wise execution of the program. This approach enables to take key features of many probabilistic programs into account: nondeterminism and conditioning. We discuss the restrictions and demonstrate the scalability on several benchmarks.

Conditioning in Probabilistic Programming

This article investigates the semantic intricacies of conditioning, a main feature in probabilistic programming. Our study is based on an extension of the imperative probabilistic guarded command language pGCL with conditioning. We provide a weakest precondition (wp) semantics and an operational semantics. To deal with possibly diverging program behavior, we consider liberal preconditions. We show that diverging program behavior plays a key role when defining conditioning. We establish that weakest preconditions coincide with conditional expected rewards in Markov chains—the operational semantics—and that the wp-semantics conservatively extends the existing semantics of pGCL (without conditioning). An extension of these results with nondeterminism turns out to be problematic: although an operational semantics using Markov decision processes is rather straightforward, we show that providing an inductive wp-semantics in this setting is impossible. Finally, we present two program transformations that eliminate conditioning from any program. The first transformation hoists conditioning while updating the probabilistic choices in the program, while the second transformation replaces conditioning—in the same vein as rejection sampling—by a program with loops. In addition, we present a last program transformation that replaces an independent identically distributed loop with conditioning.

How long, O Bayesian network, will I sample thee?

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.

Rule-Based Conditioning of Probabilistic Data

Data interoperability is a major issue in data management for data science and big data analytics. Probabilistic data integration (PDI) is a specific kind of data integration where extraction and integration problems such as inconsistency and uncertainty are handled by means of a probabilistic data representation. This allows a data integration process with two phases: (1) a quick partial integration where data quality problems are represented as uncertainty in the resulting integrated data, and (2) using the uncertain data and continuously improving its quality as more evidence is gathered. The main contribution of this paper is an iterative approach for incorporating evidence of users in the probabilistically integrated data. Evidence can be specified as hard or soft rules (i.e., rules that are uncertain themselves).