In this paper we propose an algorithm for polynomial-time reinforcement learning in factored Markov decision processes (FMDPs). The factored optimistic initial model (FOIM) algorithm, maintains an empirical model of the FMDP in a conventional way, and always follows a greedy policy with respect to its model. The only trick of the algorithm is that the model is initialized optimistically. We pro...

ROADWAY SYSTEM ASSESSMENT USING BLUETOOTH-BASED AUTOMATIC VEHICLE IDENTIFICATION TRAVEL TIME DATA This monograph is an exposition of several practice-ready methodologies for automatic vehicle identification (AVI) data collection systems. This includes considerations in the physical setup of the collection system as well as the interpretation of the data. An extended discussion is provided, with...

The formulation of multiple–time–step integrators can provide substantial computational savings for mechanical systems with multiple time scales. However, the scope of these savings may be severely limited by the range of allowable time step choices. In this paper we have performed an exhaustive study of the linear stability of the fully asynchronous methods called AVI (asynchronous variational...

Multidrug resistance of pulmonary pathogens in patients with cystic fibrosis (CF) requires new therapeutic solutions. The aim . To assess the effect ceftazidime/avibactam (cef/avi) on clinical outcomes exacerbation fibrosis. Methods This open single-group observational prospective non-interventional study was conducted routine practice settings. Patients over 18 years a confirmed diagnosis CF a...

Abstract Background Multidrug-resistant infections are challenging to treat, due underlying patient conditions, pathogen characteristics, and high resistance rates antibiotic treatments. Ceftazidime/avibactam (CAZ-AVI, approved in 2015) is treat complicated intra-abdominal infections, urinary tract hospital-acquired bacterial pneumonia, ventilator-associated pneumonia. As newer therapies come m...

We propose a sparse method for scalable automated variational inference (AVI) in a large class of models with Gaussian process (GP) priors, multiple latent functions, multiple outputs and non-linear likelihoods. Our approach maintains the statistical efficiency property of the original AVI method, requiring only expectations over univariate Gaussian distributions to approximate the posterior wi...