4 Plotly Dash apps that animate Bayesian updates of 3-dimensional Dirichlet distributions with multinomial data
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Updated
May 25, 2024 - Python
4 Plotly Dash apps that animate Bayesian updates of 3-dimensional Dirichlet distributions with multinomial data
calibrate ETAS, simulate using ETAS, estimate completeness magnitude & magnitude frequency distribution
ParaTC: Python functions and classes for generating parametric tropical cyclone models.
An implementation of latent dirichlet allocation on Wikipedia pages
Plotly Dash app that animates Bayesian updates of beta distributions with binomial data
python module, showcasing computation (as part of a learning process) of some common statistical methods including mininum sample size, confidence interval estimation methods for mean or proportion, hypothesis testing mehods and regression models witth metrics and test suites
Software to fiddle around with deep learning for phylogenetic models
"This repository serves as a comprehensive resource for understanding and applying Regression techniques in achine learning and statistical modeling."
IR-Model is an information retrieval system implementing statistical and vector space models, with a Python Flask web app and a PHP API.
A library for discrete-time Markov chains analysis.
PyForecast is a statistical modeling tool used by Reclamation water managers and reservoir operators to train and build predictive models for seasonal inflows and streamflows. PyForecast allows users to make current water-year forecasts using models developed with the program.
Statistical Digital Signal Processing and Modeling
Generic goodness of fit tests for random plain old data
Generalized Additive Models in Python.
cloze complition using N-grams in python
Statistics functions for python
Implementation of the conjugate prior table for Bayesian Statistics
💫 Models for the spaCy Natural Language Processing (NLP) library
Cegpy (/segpaɪ/) is a Python package for working with Chain Event Graphs. It supports learning the graphical structure of a Chain Event Graph from data, encoding of parametric and structural priors, estimating its parameters, and performing inference.
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