7 papers
Recursive Gaussian Processes and the Bayesian Brain
Moumita Das, Dipanjan Ray, Sourabh Bhattacharya
Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain sca…
The Bayesian Reflex: A Predictive Coding Engine for Artificial Intelligence
Sourabh Bhattacharya
Predictive coding offers a powerful theory of cortical computation, but corresponding scalable algorithmic implementations for artificial intelligence have remained elusive. This p…
The Bayesian Reflex: Online Learning as the Autonomic Nervous System of Modern and Future AI
Durba Bhattacharya, Sucharita Roy, Sourabh Bhattacharya
This chapter introduces the Bayesian reflex -- an analogy with the autonomic nervous system -- as a unifying framework for online learning in AI. Bayesian online algorithms automat…
Bayesian Nonparametrics for Gene-Gene and Gene-Environment Interactions in Case-Control Studies: A Synthesis and Extension
Durba Bhattacharya, Sourabh Bhattacharya
Gene-gene and gene-environment interactions are widely believed to play significant roles in explaining the variability of complex traits. While substantial research exists in this…
MPL-HMC: A Tunable Parameterized Leapfrog Framework for Robust Hamiltonian Monte Carlo
Sourabh Bhattacharya
This article introduces the Modified Parameterized Leapfrog Hamiltonian Monte Carlo (MPL-HMC) method, a novel extension of HMC addressing key limitations through tunable integratio…
Mean-Square Convergence of a New Parameterized Leapfrog Scheme for Hamiltonian Systems Driven by Gaussian Process Potentials
Sourabh Bhattacharya
This paper establishes the mean-square convergence of a new stochastic, parameterized leapfrog scheme introduced in our companion paper Mazumder et al. (2026) for Hamiltonian syste…