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Fernando V. Bonassi

Fernando V. Bonassi

Fernando V. Bonassi graduated with his PhD in Statistics from Duke University in 2013. He also holds BS and MS degrees in Statistics from the University of Sao Paulo (Brazil). He joined Google in 2013, where he works as quantitative analyst. His major research interests include Bayesian computation, dynamic modeling, decision analysis, among other topics.
Authored Publications
Google Publications
Other Publications
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    Bayes and Big Data: The Consensus Monte Carlo Algorithm
    Steven L. Scott
    Alexander W. Blocker
    Hugh A. Chipman
    Edward I. George
    Robert E. McCulloch
    International Journal of Management Science and Engineering Management, vol. 11 (2016), pp. 78-88
    Preview abstract A useful definition of ``big data'' is data that is too big to comfortably process on a single machine, either because of processor, memory, or disk bottlenecks. Graphics processing units can alleviate the processor bottleneck, but memory or disk bottlenecks can only be eliminated by splitting data across multiple machines. Communication between large numbers of machines is expensive (regardless of the amount of data being communicated), so there is a need for algorithms that perform distributed approximate Bayesian analyses with minimal communication. Consensus Monte Carlo operates by running a separate Monte Carlo algorithm on each machine, and then averaging individual Monte Carlo draws across machines. Depending on the model, the resulting draws can be nearly indistinguishable from the draws that would have been obtained by running a single machine algorithm for a very long time. Examples of consensus Monte Carlo are shown for simple models where single-machine solutions are available, for large single-layer hierarchical models, and for Bayesian additive regression trees (BART). View details
    Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation
    Mike West
    Bayesian Analysis, vol. 10, Number 1 (2015), pp. 171-187
    Exchangeability and the law of maturity
    Rafael B. Stern
    Claudia M. Peixoto
    Sergio Wechsler
    Theory and Decision (2014), pp. 1-13
    Bayesian Learning from Marginal Data in Bionetwork Models
    Lingchong You
    Mike West
    Statistical Applications in Genetics and Molecular Biology, vol. 10 (2011)
    In Defense of Randomization: a Subjectivist Bayesian Approach
    Raphael Nishimura
    Rafael Bassi Stern
    AIP Conference Proceedings (2009), pp. 32-39