Multichannel Double Recursive Frequentist-Bayesian Particle Identification algorithm in HEP

Categorial Naive Bayes MLE and MAP Estimators for EMNIST dataset

Jackknife with R to estimate the bias of a statistic

Treating the measurement of the same-sign W polarization fraction as a class imbalance problem

Assignments done as part of the course I have taken in Monsoon '19 at IIIT-H

Bayesian and frequentist statistics in Python with data sampled from a distribution in Scala

Data in support of... [title of manuscript]

Bachelor Thesis: Semi- and nonparametric estimation of price dispersion and auction heterogeneity with eBay auctions

Maximum likelihood estimation with TensorFlow of the parameters of an analytical model of alchemical molecular binding

This project from the series of "Statistical and Computational Methods in Physics" studies the distribution of a data based on a-priori variational distribution form and optimizing the likelihood.

The study involves predicting the attrition rate of ~72000 customers of a Telco company, and use insights from the model to develop an incentive plan for enticing would-be churners to remain with the firm. The data are available in one data file with 71,047 rows that combines the calibration and validation customers. “calibration” database consi…

Automated Car with Reinforcement Learning. Learning is done using penalty and rewards.

Selected Exercises from the course on Neural and Cognitive Modelling

Here for a small dataset we have used OLS(Ordiniary Least Square) and MLE(Maximum likelihood Estimation ) to calculate the regression parameters slope(b1),intercept(b0) and standard deviation of reisduals.At the end we can conclude that both the methods of estimation produces the same result.

Collect resources for maximum-likelihood-estimation with Github Python Examples

High Energy Physics Statistics Exercises

Research seminar about a fast selection technique for bivariate copulae.

Deep Learning Homework 1: Maximum Likelihood Estimation

implementation of some famous algorithms in statistical machine learning from scratch

Learned the fundamentals and applications in ML: Intro to Prob. & Linear algebra, Decision Theory, MLE & BE, Linear Model, Linear Discriminant function, Perceptron, FLD, PCA, Non-parametric Learning, Clustering, EM, GMM, EM and Latent Variable Model, Probabilistic Graphical Model, Bayesian Network, Neural Network, SVM, Decision Tree and Boosting