8 citations · 8 across the 4 of their papers we have counts for
6 papers
SULA: Robust Langevin Sampling via Drift-Diffusion Saturation
Ritik Soni, Dootika Vats
The unadjusted Langevin algorithm (ULA) and its stochastic-gradient variant, SGLD, are workhorses of modern Bayesian machine learning, but can be fragile when faced with a target d…
Optimal Scaling of Langevin Proposals with Generalized Acceptance Rules
Ritik Soni, Dootika Vats
Langevin-based Markov chain Monte Carlo (MCMC) algorithms use gradient information to improve sampling, particularly in high dimensions. Classical optimal scaling theory for these…
Multivariate Tempered Space-Fractional Negative Binomial Process and Risk Models with Shocks
Ashok Kumar Pathak, Ritik Soni
In this paper, we first define the multivariate tempered space-fractional Poisson process (MTSFPP) by time-changing the multivariate Poisson process with an independent tempered α-…
Generalized Fractional Risk Process
Ritik Soni, Ashok Kumar Pathak
In this paper, we define a compound generalized fractional counting process (CGFCP) which is a generalization of the compound versions of several well-known fractional counting pro…
Bivariate Tempered Space-Fractional Poisson Process and Shock Models
Ritik Soni, Ashok Kumar Pathak, Antonio Di Crescenzo +1
In this paper, we introduce a bivariate tempered space-fractional Poisson process (BTSFPP) by time-changing the bivariate Poisson process with an independent tempered -stable su…
Generalized Fractional Negative Binomial Process
Ritik Soni, Ashok Kumar Pathak
In this paper, we introduce a generalized fractional negative binomial process (GFNBP) by time changing the fractional Poisson process with an independent Mittag-Leffler (ML) Levy…