There are 0 repository under mle-estimation topic.
UW course projects
Probability Models, Detection, and ML + MMSE estimation
Maximum likelihood fits for low photon count data - For active develeopment visit gitlab.peulen.xyz
介绍和举例(正态分布、泊松分布、伽马分布)展示了极大似然估计。This paper introduces and gives examples (normal distribution, Poisson distribution, gamma distribution) to show the MLE.
MLE estimation, Linear Regression, Linear Bayesian Regression, Naive Bayes
Statistical estimation of optimal solutions for combinatorial optimization problems
A natural time analysis of the Earthquake Cycles in Taiwan by evaluating EPS scores using R and Python.
Implementation of unigram/bigram language models, noisy channel and pointwise mutual information for natural language processing.
Java based implementation of an MLE method using chi square test to calculate interference during meiotic crossover (the number of double strand dna breaks that don't result in a crossover)
Machine Learning for Data 3141 Reichman University Spring 2022 - 6 Homework Projects
A calculator that detect and estimat the heritability for discreet characters in species with known phylogeny using Markov chain Monte Carlo technique and filtering results by MLE (maximum likelihood estimation).
翻新的最大似然估计框架 copied and renewed maximum likelihood estimation package
Classification task of body positions of skeletal body movements recorded from a Kinect device (Kinect Gesture Dataset). A Bayesian approach is employed using a Linear Gaussian Model and Maximum Likelihood Estimation, assuming dependencies between skeleton joints.
Aspects of numerical analysis in the field of data science (matrix inversion, splines, function optimization, bayesian statistics, MCMC, etc)
Assignments completed for my Machine Learning course: Topics include probability and statistics proofs, MLE/MAP parameter estimation, EM Algorithm, Bayes Theorem implementations, gradient descent methods, Neural Networks and Deep Learning.
Some deep learning assignment questions, solutions and codes
The maximum likelihoood estimator approach is used here for calculating the Regression parameter that is slope(b1),intercept(b0) and standard deviation of error/residuals. Then Result or the output for the regression parameters using the OLS(ordiniary Least Sqaure) estimation method versus the MLE(MAximum Likelihood Estimation) method is compared. Also note that this MLE is used when the residual(e) of the regression model does not follow normal distribution for different observations.
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.
This is a paper dealing with truncation and censored data in the insurance agency. We go over Maximum Likelihood Estimation and the EM Algorithm for censored exponential data.
Material for Lab 12 for the course