We present a system for generating suggestions from highly-constrained, continuous design spaces. We formulate suggestion as sampling from a probability distribution; constraints are represented as factors that concentrate probability mass around sub-manifolds of the design space. These sampling problems are intractable using typical random walk MCMC techniques, so we adopt Hamiltonian Monte Ca...

Robust optimization can provide safe and tractable analytical approximation for the chance constrained optimization problem. In this work, we studied the application of robust optimization approximation in solving chance constrained planning and scheduling problem under uncertainty. Four different robust optimization approximation methods for improving the quality of robust solution were invest...

Abstract Periodic orbit theory for classical hyperbolic system is very significant matter of how we can interpret spectral statistics in terms of semiclassical theory. Although pruning is significant and generic property for almost all hyperbolic systems, pruning-proof property for the correlation among the periodic orbits which gains a resurgence of second term of the random matrix form factor...

Aims. We re-analyse the HARPS radial velocities of HD 10180 and calculate the probabilities of models with differing numbers of periodic signals in the data. We test the significance of the seven signals, corresponding to seven exoplanets orbiting the star, in the Bayesian framework and perform comparisons of models with up to nine periodicities. Methods. We used posterior samplings and Bayesia...

In this paper we study a model of gene networks introduced by Andreas Wagner in the 1990s that has been used extensively to study the evolution of mutational robustness. We investigate a range of model features and parameters and evaluate the extent to which they influence the probability that a random gene network will produce a fixed point steady state expression pattern. There are many diffe...

We consider strong and weak topologies on the space of orbits from the unit interval to the set of probability measures on a given domain. In particular, we are interested in periodic orbits of probability measures on the circle. We show that a real-valued rotation number can be defined in a natural way for all smooth enough orbits whose range is composed of probability measures supported on th...

Uncertainties may have a large impact on equipment decisions, plant operability, and economic analysis. Thus the consideration of uncertainties in optimization approaches is necessary for robust process design and operation. As a part of it, efficient chance constrained programming has become an important field of research in process systems engineering. In this work, a new approach is proposed...

Many engineering problems can be cast as optimization problems subject to convex constraints that are parameterized by an uncertainty or 'instance' parameter. Two main approaches are generally available to tackle constrained optimization problems in presence of uncertainty: robust optimization and chance-constrained optimization. Robust optimization is a deterministic paradigm where one seeks a...

The possibility, even if arbitrarily small, of binding budget constraints in simultaneous ascending induces strategic demand reduction, and generates significant inefficiencies. Under mild conditions on the distributions of the bidders’ values, unconstrained bidders behave as if they were liquidity constrained, even as the probability that bidders are budget constrained goes to zero. JEL classi...

This study aims to examine when and how readers make use of top-down information to predict or integrate upcoming words by utilizing the characteristics of Chinese classifier-noun agreement, as measured by event-related potentials (ERPs). Constraint strength of classifiers (strong and weak) and cloze probability of the pairing noun (high, low, implausible) was manipulated. Weakly constrained cl...