These are the updated probability obtained running the Python code that computes the Bayesian posterior distribution over the electoral votes using near-ignorance priors. The worst and best case distribution for Clinton are in red and respectively, blue.. Winning probability above 0.99 (for both worst and best scenario). Electoral votes between 322 and 335 (mean of …

Continue reading 28 October, USA general election situation# Python

Bayesian Sign Test Module signtest in bayesiantests computes the probabilities that, based on the measured performance, one model is better than another or vice versa or they are within the region of practical equivalence. This notebook demonstrates the use of the module. We will load the classification accuracies of the naive Bayesian classifier and AODE …

Continue reading Bayesian Sign TestI have run again the Python code that computes the worst-case (red) and best-Case (blue) posterior distribution for Clinton winning the general USA election. using fresh (September) poll-data. At the moment there is a quite large uncertainty but is still in favour of Clinton: the probability of winning is between 0.78 and 0.95. …

Continue reading Clinton vs. Trump 23th Sptember 2016We 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 2016This 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 States