An introductory neural network.
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Updated
Apr 25, 2017 - Jupyter Notebook
An introductory neural network.
Building Logistic Regression from scratch
A Python implementation of Naive Bayes from scratch.
A Python implementation of Naive Bayes from scratch. Repository influenced by https://github.com/gbroques/naive-bayes
Library for fast computation of log-likelihoods and derivatives of multivariate prior distributions
Implementing Logistic Regression for the Image Recognition task
total raw governmental industry employment data from January 1 1939 to October 30 2019. Time Series analysis to forecast employment from October 2019-October 2020.
Formulate likelihood problems and solve them with maximum likelihood estimation (MLE)
Gaussian Process Inference
This is a program written in R that finds the optimal coefficients for the arbitrary data set through minimising log-likelihood function using Gradient Descent
Robot Localization using Hidden Markov Model
Robot Localization using Hidden Markov Model
A log likelihood process for optimal entry / exit / stopping.
Classical ML algorithms implementation.
Bayesian Adaptive Direct Search (BADS) optimization algorithm for model fitting in MATLAB (old location)
Inverse binomial sampling for efficient log-likelihood estimation of simulator models (old location)
Bayesian Adaptive Direct Search (BADS) optimization algorithm for model fitting in MATLAB
Inferring likelihood and mutation rate of an evolutionary tree through the Jukes-Cantor model and Felsenstein’s algorithm
Machine Learning algorithms implemented from scratch
Likelihood-Based Inference for Time Series Extremes
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