Computational Statistics Reading Group
Webpage for Computational Statistics Reading Group
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 Thursdays at 3pm in FB567 to discuss new and old works in this area. For information on joining these meetings, please contact dootika@iitk.ac.in
Schedule for Semester II 2022-2023:
Date | Paper/Topic | Led By | Resource |
---|---|---|---|
Jan 19 | EVA2021 Challenge | Arnab Hazra | arxiv |
Feb 2 (at 2pm!) | Population Modeling Subject to Binomial Catastrophes | Nitin Kumar | Paper 1, Paper 2 |
Feb 16 | Stochastic Gradient MCMC | Apratim Shukla | |
Mar 2 | Nonconvex optimization using Nelder-Mead algorithm | Anjali Mittal | |
Mar 16 | meeting cancelled | Arghya Mukherjee | |
Mar 30 | Holiday – no meeting | ||
Apr 13 | Randomized and Exchangeable Improvements of Markov’s, Chebyshev’s and Chernoff’s Inequalities | Dootika Vats |
Schedule for Semester I 2022-2023:
Archive:
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 |