
Scalable Bayesian inference for time series via divideandconquer
Bayesian computational algorithms tend to scale poorly as data size incr...
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Bayesian neural networks and dimensionality reduction
In conducting nonlinear dimensionality reduction and feature learning, ...
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Posterior computation with the Gibbs zigzag sampler
Markov chain Monte Carlo (MCMC) sampling algorithms have dominated the l...
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Bayesian inferences on uncertain ranks and orderings
It is common to be interested in rankings or order relationships among e...
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Efficient posterior sampling for highdimensional imbalanced logistic regression
Highdimensional data are routinely collected in many application areas....
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Particle filter efficiency under limited communication
Sequential Monte Carlo (SMC) methods are typically not straightforward t...
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Deborshee Sen
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