Bayesian Modeling and Computation in Python

Last update: Jan 02, 2023

Related tags

Overview

Bayesian Modeling and Computation in Python

Open access and Code

This repository contains the open access version of the text and the code examples in the book. All of this can be more easily viewed at www.bayesiancomputationbook.com

See a mistake?

If you see any issues please create an issue on the issue tracker

Environment installation

To run the code you will need to install the correct packages in a computational environment. We have provided instructions below for common options.

Conda

conda env create -f environment.yml
conda activate bmcp

Colab

The book code can also be run using Google Colab. https://colab.research.google.com

More instructions to come soon

Owner

Bayesian Modeling and Computation in Python

Code, references and all material to accompany the text

GitHub Repository https://www.bayesiancomputationbook.com

Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm.

Naive-Bayes Spam Classificator Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm. Main goal is to code a

1 Jun 27, 2022

MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions.

Using python and scikit-learn to make stock predictions

1.3k Jan 03, 2023

Simplify stop motion animation with machine learning.

25 Sep 15, 2022

Machine Learning Algorithms

Machine-Learning-Algorithms In this project, the dataset was created through a survey opened on Google forms. The purpose of the form is to find the p

3 Aug 10, 2022

Confidence intervals for scikit-learn forest algorithms

forest-confidence-interval: Confidence intervals for Forest algorithms Forest algorithms are powerful ensemble methods for classification and regressi

272 Dec 01, 2022

An open-source library of algorithms to analyse time series in GPU and CPU.

216 Dec 30, 2022

whylogs: A Data and Machine Learning Logging Standard

whylogs: A Data and Machine Learning Logging Standard whylogs is an open source standard for data and ML logging whylogs logging agent is the easiest

2k Jan 06, 2023

A logistic regression model for health insurance purchasing prediction

Logistic_Regression_Model A logistic regression model for health insurance purchasing prediction This code is using these packages, so please make sur

1 Nov 29, 2021

A basic Ray Tracer that exploits numpy arrays and functions to work fast.

Python-Fast-Raytracer A basic Ray Tracer that exploits numpy arrays and functions to work fast. The code is written keeping as much readability as pos

393 Dec 27, 2022

DaCeML - Machine learning powered by data-centric parallel programming.

Data-centric machine learning powered by DaCe

48 Dec 12, 2022

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

OptaPy is an AI constraint solver for Python to optimize the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference S

208 Dec 27, 2022

Decision Weights in Prospect Theory

Decision Weights in Prospect Theory It's clear that humans are irrational, but how irrational are they? After some research into behavourial economics

32 Nov 08, 2021

Python implementation of the rulefit algorithm

RuleFit Implementation of a rule based prediction algorithm based on the rulefit algorithm from Friedman and Popescu (PDF) The algorithm can be used f

326 Jan 02, 2023

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically

FLAML - Fast and Lightweight AutoML

2.2k Jan 09, 2023

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

2 Jan 18, 2022

Bayesian Modeling and Computation in Python

Related tags

Overview

Bayesian Modeling and Computation in Python

Open access and Code

See a mistake?

Environment installation

Conda

Colab

Owner

Bayesian Modeling and Computation in Python

Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm.

MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions.

Simplify stop motion animation with machine learning.

Machine Learning Algorithms

Confidence intervals for scikit-learn forest algorithms

An open-source library of algorithms to analyse time series in GPU and CPU.

whylogs: A Data and Machine Learning Logging Standard

A logistic regression model for health insurance purchasing prediction

A basic Ray Tracer that exploits numpy arrays and functions to work fast.

DaCeML - Machine learning powered by data-centric parallel programming.

OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

Decision Weights in Prospect Theory

Python implementation of the rulefit algorithm

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically

A Streamlit demo to interactively visualize Uber pickups in New York City

BioPy is a collection (in-progress) of biologically-inspired algorithms written in Python

ml4ir: Machine Learning for Information Retrieval

Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

Bayesian optimization in JAX

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.