2,560 research outputs found
Compositional Semantics for Probabilistic Programs with Exact Conditioning
We define a probabilistic programming language for Gaussian random variables
with a first-class exact conditioning construct. We give operational,
denotational and equational semantics for this language, establishing
convenient properties like exchangeability of conditions. Conditioning on
equality of continuous random variables is nontrivial, as the exact observation
may have probability zero; this is Borel's paradox. Using categorical
formulations of conditional probability, we show that the good properties of
our language are not particular to Gaussians, but can be derived from universal
properties, thus generalizing to wider settings. We define the Cond
construction, which internalizes conditioning as a morphism, providing general
compositional semantics for probabilistic programming with exact conditioning.Comment: 16 pages, 5 figure
Formal verification of higher-order probabilistic programs
Probabilistic programming provides a convenient lingua franca for writing
succinct and rigorous descriptions of probabilistic models and inference tasks.
Several probabilistic programming languages, including Anglican, Church or
Hakaru, derive their expressiveness from a powerful combination of continuous
distributions, conditioning, and higher-order functions. Although very
important for practical applications, these combined features raise fundamental
challenges for program semantics and verification. Several recent works offer
promising answers to these challenges, but their primary focus is on semantical
issues.
In this paper, we take a step further and we develop a set of program logics,
named PPV, for proving properties of programs written in an expressive
probabilistic higher-order language with continuous distributions and operators
for conditioning distributions by real-valued functions. Pleasingly, our
program logics retain the comfortable reasoning style of informal proofs thanks
to carefully selected axiomatizations of key results from probability theory.
The versatility of our logics is illustrated through the formal verification of
several intricate examples from statistics, probabilistic inference, and
machine learning. We further show the expressiveness of our logics by giving
sound embeddings of existing logics. In particular, we do this in a parametric
way by showing how the semantics idea of (unary and relational) TT-lifting can
be internalized in our logics. The soundness of PPV follows by interpreting
programs and assertions in quasi-Borel spaces (QBS), a recently proposed
variant of Borel spaces with a good structure for interpreting higher order
probabilistic programs
Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures
Bayesian Network, Ahmed Rebai (Ed.), ISBN: 978-953-307-124-4, pp. 13-38
The geometry of Bayesian programming
We give two geometry of interaction models for a typed λ-calculus with recursion endowed with operators for sampling from a continuous uniform distribution and soft conditioning, namely a paradigmatic calculus for higher-order Bayesian programming. The models are based on the category of measurable spaces and partial measurable functions, and the category of measurable spaces and s-finite kernels, respectively. The former is proved adequate with respect to both a distribution-based and a sampling-based operational semantics, while the latter is proved adequate with respect to a sampling-based operational semantics
Learning Bayesian network equivalence classes using ant colony optimisation
Bayesian networks have become an indispensable tool in the modelling of uncertain
knowledge. Conceptually, they consist of two parts: a directed acyclic graph called the
structure, and conditional probability distributions attached to each node known as the
parameters. As a result of their expressiveness, understandability and rigorous mathematical basis, Bayesian networks have become one of the first methods investigated,
when faced with an uncertain problem domain. However, a recurring problem persists
in specifying a Bayesian network. Both the structure and parameters can be difficult for
experts to conceive, especially if their knowledge is tacit.To counteract these problems, research has been ongoing, on learning both the structure
and parameters of Bayesian networks from data. Whilst there are simple methods for
learning the parameters, learning the structure has proved harder. Part ofthis stems from
the NP-hardness of the problem and the super-exponential space of possible structures.
To help solve this task, this thesis seeks to employ a relatively new technique, that has
had much success in tackling NP-hard problems. This technique is called ant colony
optimisation. Ant colony optimisation is a metaheuristic based on the behaviour of ants
acting together in a colony. It uses the stochastic activity of artificial ants to find good
solutions to combinatorial optimisation problems. In the current work, this method is
applied to the problem of searching through the space of equivalence classes of Bayesian
networks, in order to find a good match against a set of data. The system uses operators
that evaluate potential modifications to a current state. Each of the modifications is
scored and the results used to inform the search. In order to facilitate these steps, other
techniques are also devised, to speed up the learning process. The techniques includeThe techniques are tested by sampling data from gold standard networks and learning
structures from this sampled data. These structures are analysed using various goodnessof-fit measures to see how well the algorithms perform. The measures include structural
similarity metrics and Bayesian scoring metrics. The results are compared in depth
against systems that also use ant colony optimisation and other methods, including
evolutionary programming and greedy heuristics. Also, comparisons are made to well
known state-of-the-art algorithms and a study performed on a real-life data set. The
results show favourable performance compared to the other methods and on modelling
the real-life data
Nonparametric Hamiltonian Monte Carlo
Probabilistic programming uses programs to express generative models whose
posterior probability is then computed by built-in inference engines. A
challenging goal is to develop general purpose inference algorithms that work
out-of-the-box for arbitrary programs in a universal probabilistic programming
language (PPL). The densities defined by such programs, which may use
stochastic branching and recursion, are (in general) nonparametric, in the
sense that they correspond to models on an infinite-dimensional parameter
space. However standard inference algorithms, such as the Hamiltonian Monte
Carlo (HMC) algorithm, target distributions with a fixed number of parameters.
This paper introduces the Nonparametric Hamiltonian Monte Carlo (NP-HMC)
algorithm which generalises HMC to nonparametric models. Inputs to NP-HMC are a
new class of measurable functions called "tree representable", which serve as a
language-independent representation of the density functions of probabilistic
programs in a universal PPL. We provide a correctness proof of NP-HMC, and
empirically demonstrate significant performance improvements over existing
approaches on several nonparametric examples.Comment: Updated plots (after fixing minor bugs in the implementation)
compared to the published version in Proceedings of the 38th International
Conference on Machine Learning, PMLR 139, 2021. The conclusions of the
version published at ICML 2021 are not affecte
- …