6 citations · 18 across the 9 of their papers we have counts for
11 papers
Scalable Bayesian inference for time series via divide-and-conquer
Rihui Ou, Lachlan Astfalck, Deborshee Sen +1
Bayesian computational algorithms tend to scale poorly as data size increases. This has motivated divide-and-conquer-based approaches for scalable inference. These divide the data…
Bayesian neural networks and dimensionality reduction
Deborshee Sen, Theodore Papamarkou, David Dunson
In conducting non-linear dimensionality reduction and feature learning, it is common to suppose that the data lie near a lower-dimensional manifold. A class of model-based approach…
Posterior computation with the Gibbs zig-zag sampler
Matthias Sachs, Deborshee Sen, Jianfeng Lu +1
An intriguing new class of piecewise deterministic Markov processes (PDMPs) has recently been proposed as an alternative to Markov chain Monte Carlo (MCMC). In order to facilitate…
Bayesian inferences on uncertain ranks and orderings: Application to ranking players and lineups
Andres F. Barrientos, Deborshee Sen, Garritt L Page +1
It is common to be interested in rankings or order relationships among entities. In complex settings where one does not directly measure a univariate statistic upon which to base r…
Efficient posterior sampling for high-dimensional imbalanced logistic regression
Deborshee Sen, Matthias Sachs, Jianfeng Lu +1
High-dimensional data are routinely collected in many areas. We are particularly interested in Bayesian classification models in which one or more variables are imbalanced. Current…
Particle filter efficiency under limited communication
Deborshee Sen
Sequential Monte Carlo methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between a…
Posterior Projection for Inference in Constrained Spaces
Lachlan Astfalck, Deborshee Sen, Sayan Patra +2
Estimation of parameters that obey specific constraints is crucial in statistics and machine learning; for example, when parameters are required to satisfy boundedness, monotonicit…
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states
Rihui Ou, Deborshee Sen, Alexander L Young +1
Markov chain Monte Carlo (MCMC) algorithms for hidden Markov models often rely on the forward-backward sampler. This makes them computationally slow as the length of the time serie…
Clustering sequence data with mixture Markov chains with covariates using multiple simplex constrained optimization routine (MSiCOR)
Priyam Das, Deborshee Sen, Debsurya De +5
Mixture Markov Model (MMM) is a widely used tool to cluster sequences of events coming from a finite state-space. However the MMM likelihood being multi-modal, the challenge remain…
Some Contributions to Sequential Monte Carlo Methods for Option Pricing
Deborshee Sen, Ajay Jasra, Yan Zhou
Pricing options is an important problem in financial engineering. In many scenarios of practical interest, financial option prices associated to an underlying asset reduces to comp…
On Coupling Particle Filter Trajectories
Deborshee Sen, Alexandre Thiery, Ajay Jasra
Particle filters are a powerful and flexible tool for performing inference on state-space models. They involve a collection of samples evolving over time through a combination of s…