We continue our adventure in the Bayesian USA 2016 election forecast through near-ignorance priors. I will today show how to compute the lower and upepr probabilities for Clinton of winning the general election 2016. First, we load the lower and upper probabilities for Clinton of winning in every single State (see bayesian-winning-lower-and-upper/) as well as …

Continue reading General Poll for US Presidential Election 2016# coding

This post is about how to perform a Bayesian analysis of election polls for USA 2016 presidential election. In previous posts, I have discussed how to make a poll for a single State (Nevada as example). Here we will use some simple Python functions to compue the probability for Clinton of winning in all 51 …

Continue reading Bayesian winning lower and upper probabilities in all 51 StatesI will show how to apply the models described in a-description-of-bayesian-near_ignorance_prior to predict USA2016 election results in Nevada. The polls data are from www.realclearpolitics.com, in particular KTNV/Rasmussen poll (see below). In a future post, I will discuss how to take into account of the three polls. We start by importing the data. In [4]: import pandas …

Continue reading Nevada data poll with near-ignorance priors and Python