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Modal Logics for Nominal Transition Systems

Joachim Parrow ; Johannes Borgström ; Lars-Henrik Eriksson ; Ramūnas Forsberg Gutkovas ; Tjark Weber.

We define a general notion of transition system where states and action labels can be from arbitrary nominal sets, actions may bind names, and state predicates from an arbitrary logic define properties of states. A Hennessy-Milner logic for these systems is introduced, and proved adequate and&nbsp;[&hellip;]

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Measure Transformer Semantics for Bayesian Machine Learning

Johannes Borgström ; Andrew D Gordon ; Michael Greenberg ; James Margetson ; Jurgen Van Gael.

The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing&nbsp;[&hellip;]

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A Sorted Semantic Framework for Applied Process Calculi

Johannes Borgström ; Ramūnas Gutkovas ; Joachim Parrow ; Björn Victor ; Johannes Åman Pohjola.

Applied process calculi include advanced programming constructs such as type systems, communication with pattern matching, encryption primitives, concurrent constraints, nondeterminism, process creation, and dynamic connection topologies. Several such formalisms, e.g. the applied pi calculus, are&nbsp;[&hellip;]

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Deriving Probability Density Functions from Probabilistic Functional Programs

Sooraj Bhat ; Johannes Borgström ; Andrew D. Gordon ; Claudio Russo.

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently&nbsp;[&hellip;]

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