Doubly Stochastic Generative Arrivals Modeling

Learning Deep Generative Models with Doubly Stochastic MCMC

We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models in the collapsed continuous parameter space. At each MCMC sampling step, the algorithm randomly draws a minibatch of data samples to estimate the gradient of log-posterior and further estimates the intractable expectation over latent variables via a Gibbs sample...

Monotonicity Results for Queues with Doubly Stochastic Poisson Arrivals: Ross's Conjecture

Parametrizing Doubly Stochastic Measures

Doubly stochastic measures can be identi ed with the trace of a pair of (Lebesgue) measure preserving maps of the unit interval to itself. It has been of traditional interest in probability theory to produce a random vector (or metric space element), which has a given distribution and is de ned on a standard space, such as [0; 1] endowed with Lebesgue measure. In a classic work, L evy [4, secti...

GSNs : Generative Stochastic Networks

We introduce a novel training principle for generative probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework generalizes Denoising Auto-Encoders (DAE) and is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. The transition distribution is a conditiona...

Modeling Ship Arrivals in Ports

Ports provide jetty facilities for ships to load and unload their cargo. Since ship delays are costly, terminal operators attempt to minimize their number and duration. Here, simulation has proved to be a very suitable tool. However, in port simulation models, the impact of the arrival process of ships on the model outcomes tends to be underestimated. This article considers three arrival proces...