Metadata-Version: 1.0
Name: BayesianLinearRegression
Version: 0.1.2
Summary: Fits Linear and Logistic Regression Models using MCMC.
Home-page: UNKNOWN
Author: John Clements
Author-email: blr.johnclements@gmail.com
License: UNKNOWN
Description: # BayesianLinearRegression
        
        This project was for me to gain a better understanding of the Metropolis-Hastings algorithm and work on my object-oriented programming skills. If you need to do any Bayesian modeling in Python, I recommend using PyMC3 (https://docs.pymc.io/). 
        
        #### -- Project Status: [Completed]
        
        ## Project Intro/Objective
        
        The purpose of this project was for me to work with the Metropolis-Hastings algorithm and get comfortable with object-oriented programming.
        
        ### Methods Used
        
        * Generalized Linear Models
        * Bayesian Statistics
        * Metropolis-Hastings Markov Chain Monte Carlo
        * Object-Oriented Programming
        
        ### Technologies
        
        * Python (NumPy, SciPy, tqdm)
        
        ## Project Description
        
        I created a class called MetropolisHastingsLinearModel, which is the parent class of the GaussianModel, LaplacianModel, and LogisticModel classes. It has  methods to calculate the log-prior (assuming normal priors for the distributions of the coefficients in the model), the log-posterior, fit the model/simulate the posteriors of the coefficients, burn the first x% the simulated distribution, set credible intervals, and predict for new data. For the GaussianModel and LaplacianModel classes, I only needed to add a log-likelihood method corresponding to those distributions. For the LogisticModel class, I added a method implementing the inverse-logit transformation for use in calculating the log-likelihood of the coefficients given the data and for predicting the probabilities of new observations. In addition to the method for predicting probabilities, I implemented a method for predicting the classes of new observations.
Platform: UNKNOWN
