Reuse, recycle, reweigh: Combating influenza through efficient sequential Bayesian computation for massive data

Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive Data.

Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data c...

Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive Data1 by Jennifer A. Tom,

Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data c...

Recycle, Reduce, Reuse

Variable Reuse for Efficient Image Computation

Image computation, that is, computing the set of states reachable from a given set in one step, is a crucial component in typical tools for BDD-based symbolic reachability analysis. It has been shown that the size of the intermediate BDDs during image computation can be dramatically reduced via conjunctive partitioning of the transition relation and ordering the conjuncts for facilitating early...

Sequential Monte Carlo for Bayesian Computation

Sequential Monte Carlo (SMC) methods are a class of importance sampling and resampling techniques designed to simulate from a sequence of probability distributions. These approaches have become very popular over the last few years to solve sequential Bayesian inference problems (e.g. Doucet et al. 2001). However, in comparison to Markov chain Monte Carlo (MCMC), the application of SMC remains l...