Case studies with Bayesian methods

Overview

bayesian-modeling

Case studies with Bayesian methods

Hierarchical model for spelling performance

A hierarchical model for the spelling data from the study D. Thissen, Steinberg, and Wainer (1993)

average actor rates

Multinomial logistic model for three-class classifition

A multinomial logistic model for the iris dataset (Fisher, 1936)

sepal evaluation

Fourier analysis for time series

A Fourier analysis model for the Mauna Loa CO2 data (the Keeling curve)

keeling curve

Time series modeling with heteroskedastic data

A hierarchical time series model with log-normal likelihood for air polution data in Skopje.

log-normal time-series

Inference with weights on observed data

A simple example using Potential for inference with data with weights

large poisson dataset

Monotone dependency modeling

A simple example using Dirichlet prior for modeling of monotone dependency

monotone dependency

Slow evolving multiplicative factor - Covid-19 Rt model

A model for estimation of the Rt reproductive factor for Covid-19, modeled with Random walks in the log scale.

covid 19 Rt factor

Owner
Baze Petrushev
Data Scientist playing around with Python and Spark
Baze Petrushev
Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

EconML/CausalML KDD 2021 Tutorial 124 Dec 28, 2022
Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Thines Kumar 1 Jan 31, 2022
Simulate & classify transient absorption spectroscopy (TAS) spectral features for bulk semiconducting materials (Post-DFT)

PyTASER PyTASER is a Python (3.9+) library and set of command-line tools for classifying spectral features in bulk materials, post-DFT. The goal of th

Materials Design Group 4 Dec 27, 2022
Book Recommender System Using Sci-kit learn N-neighbours

Model-Based-Recommender-Engine I created a book Recommender System using Sci-kit learn's N-neighbours algorithm for my model and the streamlit library

1 Jan 13, 2022
使用数学和计算机知识投机倒把

偷鸡不成项目集锦 坦率地讲,涉及金融市场的好策略如果公开,必然导致使用的人多,最后策略变差。所以这个仓库只收集我目前失败了的案例。 加密货币组合套利 中国体育彩票预测 我赚不上钱的项目,也许可以帮助更有能力的人去赚钱。

Roy 28 Dec 29, 2022
Learn Machine Learning Algorithms by doing projects in Python and R Programming Language

Learn Machine Learning Algorithms by doing projects in Python and R Programming Language. This repo covers all aspect of Machine Learning Algorithms.

Ravi Chaubey 6 Oct 20, 2022
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
SIMD-accelerated bitwise hamming distance Python module for hexidecimal strings

hexhamming What does it do? This module performs a fast bitwise hamming distance of two hexadecimal strings. This looks like: DEADBEEF = 1101111010101

Michael Recachinas 12 Oct 14, 2022
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin

Microsoft 8.4k Dec 30, 2022
This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing variance.

minvar_invest_portfolio This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing var

1 Jan 06, 2022
A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

Daniel Formoso 5.7k Dec 30, 2022
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2021 Links Doc

Sebastian Raschka 4.2k Dec 29, 2022
This jupyter notebook project was completed by me and my friend using the dataset from Kaggle

ARM This jupyter notebook project was completed by me and my friend using the dataset from Kaggle. The world Happiness 2017, which ranks 155 countries

1 Jan 23, 2022
Multiple Linear Regression using the LinearRegression class from sklearn.linear_model library

Multiple-Linear-Regression-master - A python program to implement Multiple Linear Regression using the LinearRegression class from sklearn.linear model library

Kushal Shingote 1 Feb 06, 2022
We have a dataset of user performances. The project is to develop a machine learning model that will predict the salaries of baseball players.

Salary-Prediction-with-Machine-Learning 1. Business Problem Can a machine learning project be implemented to estimate the salaries of baseball players

Ayşe Nur Türkaslan 9 Oct 14, 2022
A Python package for time series classification

pyts: a Python package for time series classification pyts is a Python package for time series classification. It aims to make time series classificat

Johann Faouzi 1.4k Jan 01, 2023
A Lightweight Hyperparameter Optimization Tool 🚀

The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machine Learning Experiment (MLE) pipeline.

Robert Lange 137 Dec 02, 2022
A Python toolkit for rule-based/unsupervised anomaly detection in time series

Anomaly Detection Toolkit (ADTK) Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised / rule-based time series anomaly detection. As

Arundo Analytics 888 Dec 30, 2022
A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

abdelhak mokri 1 Dec 20, 2021
Automatically create Faiss knn indices with the most optimal similarity search parameters.

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

Criteo 419 Jan 01, 2023