We present an approach to probabilistic analysis which is based on program semantics and exploits the mathematical properties of the semantical operators to ensure a form of optimality for the analysis. As in the algorithmic setting, where the analysis results are used the help the design of efficient algorithms, the purposes of our framework are to offer static analysis techniques usable for resource optimisation
We present a new semantics sensitive sampling algorithm for probabilistic pro-grams, which are “usua...
In this paper we start by reviewing both classical and probabilistic/quantitative approaches to prog...
Abstract Probabilistic programming languages allow programmers to write down conditional probability...
Abstract. We present an approach to probabilistic analysis which is based on program semantics and e...
The aims of these lecture notes are two-fold: (i) we investigate the relation between the operationa...
Abstract. For a simple probabilistic language we present a semantics based on linear operators on in...
We present a semantics-based technique for analysing probabilistic properties of imperative programs...
AbstractWe present a semantics-based technique for analysing probabilistic properties of imperative ...
We outline a general purpose framework for black-box marginal maximum a pos- teriori estimation of p...
We study quantitative reasoning about probabilistic programs. In doing so, we investigate two main a...
Abstract. Having a precise yet sound abstraction of the inputs of nu-merical programs is important t...
The work is supported by the EPSRC. Abstract. In this paper we show how quantitative program logic [...
We present a formal framework for syntax directed probabilistic program analysis. Our focus is on pr...
Abstract. We provide a HOL formalisation for analysing expected time bounds for probabilistic progra...
Probabilistic programs [6] are sequential programs, written in languages like C, Java, Scala, or ML,...
We present a new semantics sensitive sampling algorithm for probabilistic pro-grams, which are “usua...
In this paper we start by reviewing both classical and probabilistic/quantitative approaches to prog...
Abstract Probabilistic programming languages allow programmers to write down conditional probability...
Abstract. We present an approach to probabilistic analysis which is based on program semantics and e...
The aims of these lecture notes are two-fold: (i) we investigate the relation between the operationa...
Abstract. For a simple probabilistic language we present a semantics based on linear operators on in...
We present a semantics-based technique for analysing probabilistic properties of imperative programs...
AbstractWe present a semantics-based technique for analysing probabilistic properties of imperative ...
We outline a general purpose framework for black-box marginal maximum a pos- teriori estimation of p...
We study quantitative reasoning about probabilistic programs. In doing so, we investigate two main a...
Abstract. Having a precise yet sound abstraction of the inputs of nu-merical programs is important t...
The work is supported by the EPSRC. Abstract. In this paper we show how quantitative program logic [...
We present a formal framework for syntax directed probabilistic program analysis. Our focus is on pr...
Abstract. We provide a HOL formalisation for analysing expected time bounds for probabilistic progra...
Probabilistic programs [6] are sequential programs, written in languages like C, Java, Scala, or ML,...
We present a new semantics sensitive sampling algorithm for probabilistic pro-grams, which are “usua...
In this paper we start by reviewing both classical and probabilistic/quantitative approaches to prog...
Abstract Probabilistic programming languages allow programmers to write down conditional probability...