Pure Numpy Implementation of the Coherent Point Drift Algorithm
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Updated
Aug 8, 2023 - Python
Pure Numpy Implementation of the Coherent Point Drift Algorithm
An implementation of the expectation maximization algorithm
Python library to implement advanced trading strategies using machine learning and perform backtesting.
Code for the algorithms in the paper: Vaibhav B Sinha, Sukrut Rao, Vineeth N Balasubramanian. Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification. KDD WISDOM 2018
Code and data for the KDD2020 paper "Learning Opinion Dynamics From Social Traces"
CLIP-seq Analysis of Multi-mapped reads
Learning Bayesian Network parameters using Expectation-Maximisation
GPU traning of a Gaussian Mixture (with online EM)
A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.
My works for EE 511 - Simulation Methods For Stochastic Systems - Spring 2018 - Graduate Coursework at USC - Dr. Osonde A. Osoba
Python code to fit Gaussian Mixture Models to data using expectation maximization
Code developed for CSE 515 Multimedia Web Databases
GenMM
Brain tissue segmentation using several algorithms: EM, Atlas-based methods, nnunet
Modelling Bach Chorales using Factorial Hidden Markov Models
Source repo for data linking application. Requires ligo-lib.
Medical Image Segmentation and Applications (MISA) LAB task.
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