This is the webpage of the Computational Statistics Reading group, IIT Kanpur. The group meets to discuss new and old works in the broad area of Computational Statistics.

Broad topics of interest include (but are not limited to):

  • Statistical Optimization
  • Monte Carlo
  • Bayesian computation
  • Bootstrap
  • Cross-validation
  • Density estimation
  • Visualization
  • Reproducibility

Prerequisites: Although coding will not be a primary component of the meetings, whenever needed we may discuss coding implementation issues in R. Additionally, a basic understanding of Markov chains, optimization methods, and broad-spectrum statistics will be helpful.

The group meets every two weeks on Fridays at 3pm on Zoom to discuss new and old works in this area. For information on joining these meetings, please contact

Next meeting: April 8, 2022 at 3:00pm

Paper: Approximate Bayesian Computation, Marin et. al. (2011), Lee (2012)

Led by: Vansh Bansal


Date Paper/Topic Led By Resource
25/03/22 Multivariate Quantiles Subhra Sankar Dhar Notes
04/03/22 Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations Arnab Hazra  
18/02/22 Statistical inference for model parameters in stochastic gradient descent by Chen et. al. Rahul Singh slides
11/02/22 Introduction to Stochastic Gradient Descent, Paper: Robbins and Monro, Polyak and Juditsky Abhinek Shukla -
28/01/22 “Density Estimation for the Metropolis-Hastings Algorithm” by Sköld and Roberts Dootika Vats Codes
03/11/21 “Cross-validation: what does it estimate and how well does it do it?” by Bates, Hastie, Tibshirani Dootika Vats Codes
26/10/21 Numerical aspects of Stochastic Differential Equations Akash Anand Slides, Codes
19/10/21 Numerical aspects of Stochastic Differential Equations Akash Anand - Numerical Solution of Stochastic Differential Equation by Peter E. Kloeden and Eckhard Platen
05/10/21 Motivating Stochastic Differential Equations - 2 Suprio Bhar Stochastic Differential Equations by Bernt Oksendal (Chapter 1), Foundations of the Theory of Semilinear Stochastic Partial Differential Equations
28/09/21 Motivating Stochastic Differential Equations - 1 Suprio Bhar Notes on Brownian Motion by Curien Nicolas, Stochastic Differential Equations by Bernt Oksendal (Chapter 1)
21/09/21 “Fast sampling with Gaussian scale mixture priors in high-dimensional regression”(2016) by Anirban Bhattacharya, Antik Chakraborty, Bani K. Mallick Online Dootika Vats Simulations
07/09/21 “Some Asymptotic Theory for the Bootstrap” by Bickel and Freedman (1981) Online Abhinek Shukla and Rahul Singh Slides 1 Slides 2
24/08/21 “What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum” by Tim Hesterberg (2015)Online Dootika Vats and Agamani Saha R code