48 citations · 126 across the 51 of their papers we have counts for
14 papers · 1 filter
A New Spatio-Temporal Model Exploiting Hamiltonian Equations
Satyaki Mazumder, Sayantan Banerjee, Sourabh Bhattacharya
The solutions of Hamiltonian equations are known to describe the underlying phase space of a mechanical system. In this article, we propose a novel spatio-temporal model using a st…
IID Sampling from Intractable Multimodal and Variable-Dimensional Distributions
Sourabh Bhattacharya
Bhattacharya (2021b) has introduced a novel methodology for generating iid realizations from any target distribution on the Euclidean space, irrespective of dimensionality. In this…
Bayesian Levy-Dynamic Spatio-Temporal Process: Towards Big Data Analysis
Sourabh Bhattacharya
In this era of big data, all scientific disciplines are evolving fast to cope up with the enormity of the available information. So is statistics, the queen of science. Big data ar…
A Statistical Perspective on Inverse and Inverse Regression Problems
Debashis Chatterjee, Sourabh Bhattacharya
Inverse problems, where in broad sense the task is to learn from the noisy response about some unknown function, usually represented as the argument of some known functional form,…
Nonparametric Dynamic State Space Modeling of Observed Circular Time Series with Circular Latent States: A Bayesian Perspective
Satyaki Mazumder, Sourabh Bhattacharya
Circular time series has received relatively little attention in statistics and modeling complex circular time series using the state space approach is non-existent in the literatu…
On Single Variable Transformation Approach to Markov Chain Monte Carlo
Kushal K. Dey, Sourabh Bhattacharya
Random Walk Metropolis Hastings (RWMH) algorithm, is quite inefficient in high dimensions because of its abysmally slow acceptance rate. The slow acceptance rate results from the f…
Bayesian Nonparametric Dynamic State Space Modeling with Circular Latent States
Satyaki Mazumder, Sourabh Bhattacharya
State space models are well-known for their versatility in modeling dynamic systems that arise in various scientific disciplines. Although parametric state space models are well st…
Gaussian Random Functional Dynamic Spatio-Temporal Modeling of Discrete Time Spatial Time Series Data
Suman Guha, Sourabh Bhattacharya
Discrete time spatial time series data arise routinely in meteorological and environmental studies. Inference and prediction associated with them are mostly carried out using any o…
Nonstationary, Nonparametric, Nonseparable Bayesian Spatio-Temporal Modeling Using Kernel Convolution of Order Based Dependent Dirichlet Process
Moumita Das, Sourabh Bhattacharya
In this article, using kernel convolution of order based dependent Dirichlet process (Griffin and Steel (2006)) we construct a nonstationary, nonseparable, nonparametric space-time…
On Geometric Ergodicity of Additive and Multiplicative Transformation Based Markov Chain Monte Carlo in High Dimensions
Kushal Kumar Dey, Sourabh Bhattacharya
Recently Dutta and Bhattacharya (2013) introduced a novel Markov Chain Monte Carlo methodology that can simultaneously update all the components of high dimensional parameters usin…
Clustering Categorical Time Series into Unknown Number of Clusters: A Perfect Simulation based Approach
Sabyasachi Mukhopadhyay, Sourabh Bhattacharya
Pamminger and Fruwirth-Schnatter (2010) considered a Bayesian approach to model-based clustering of categorical time series assuming a fixed number of clusters. But the popular met…
Non-marginal Decisions: A Novel Bayesian Multiple Testing Procedure
Noirrit K. Chandra, Sourabh Bhattacharya
In this paper we consider the problem of multiple testing when the hypotheses are dependent. In most of the existing literature, either Bayesian or non-Bayesian, the decision rules…
A Fully Bayesian Approach to Assessment of Model Adequacy in Inverse Problems
Sourabh Bhattacharya
We consider the problem of assessing goodness of fit of a single Bayesian model to the observed data in the inverse problem context. A novel procedure of goodness of fit test is pr…
Bayesian Inference in Nonparametric Dynamic State-Space Models
Anurag Ghosh, Soumalya Mukhopadhyay, Sandipan Roy +1
We introduce state-space models where the functionals of the observational and the evolutionary equations are unknown, and treated as random functions evolving with time. Thus, our…