Multichannel Double Recursive Frequentist-Bayesian Particle Identification algorithm in HEP
-
Updated
Feb 11, 2018 - C++
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
Implementing the Linkage and Pathway Universal Tests using the clrdag package for Direceted Acyclic Graphs. Testing the two tests on cytometry data to analyze the links.
Distribution Parameter Estimation
Arbitrage-free Dynamic Generalized Nelson-Siegel model of interest rates following Christensen, Diebold and Rudebusch; and its estimation using the Kalman filter / maximum likelihood.
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
Add a description, image, and links to the maximum-likelihood-estimation topic page so that developers can more easily learn about it.
To associate your repository with the maximum-likelihood-estimation topic, visit your repo's landing page and select "manage topics."