Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data
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DOI: 10.1007/s11222-020-09980-4
Abstract: The Hawkes process and its extensions effectively model self-excitatory phenomena including earthquakes, viral pandemics, financial transactions, neural spike trains and the spread of memes through social networks. The usefulness of these stochastic process models within… read more here.
Keywords: bayesian inference; stochastic processes; inference self; scalable bayesian ... See more keywords
Scalable Bayesian Nonparametric Clustering and Classification
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DOI: 10.1080/10618600.2019.1624366
Abstract: Abstract We develop a scalable multistep Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel” and can be implemented using the same… read more here.
Keywords: bayesian nonparametric; nonparametric clustering; scalable bayesian; clustering classification ... See more keywords
A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization
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DOI: 10.3390/e23111426
Abstract: Stochastic gradient sg-based algorithms for Markov chain Monte Carlo sampling (sgmcmc) tackle large-scale Bayesian modeling problems by operating on mini-batches and injecting noise on sgsteps. The sampling properties of these algorithms are determined by user… read more here.
Keywords: bayesian sampling; scalable bayesian; gradient; stochastic gradient ... See more keywords