Many probabilistic programming languages allow programs to be run under constraints in order to carry out Bayesian inference. Running programs under constraints could enable other uses such as rare event simulation and probabilistic verification—except that all such probabilistic languages are necessarily limited because they are defined or implemented in terms of an impoverished theory of prob...

This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law incorporating dynamic feedback gain to minimise quadratic cost function subject single chance constraint. The is selected online we provide two selection methods based on minimising upper bounds predicted costs. constraint defined as discounted sum of violation probabil...

This paper presents a probabilistic algorithm that extracts a mapping between two subspaces by representing each subspace as a collection of states. In many cases, the data is a time series with temporal constraints. This paper suggests a method to impose these temporal constraints on the transitions between the states of the subspace. This probabilistic model has been successfully applied to t...