## 28 October, USA general election situation

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 …

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## Fresh forecast for US2016 election

The worst-case probability for Clinton winning the election is back above 90% (precisely 93%). You can try by yourself running this code  in my Github repository.

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## US2016 election forecast

I have run again the Bayesian algorithm that uses a prior near-ignorance model to compute US2016 election forecast. This is the current situation for Clinton (worst-case in red and best-case in blue). The probability range of winning the election (by getting the majority of the electoral votes) is [0.68,0.91]. The …

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## Clinton vs. Trump 23th Sptember 2016

I 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 …

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## General Poll for US Presidential Election 2016

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 …

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## Bayesian winning lower and upper probabilities in all 51 States

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 …

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## Combining polls data from different sources using covariance intersection

In a previous post, we have seen how to perform polls for a single State using poll data from KTNV/Rasmussen. Here  we are going to see how to combine polls from different sources. Let us consider again Nevada polls. Poll Date Sample MoE Clinton (D) Trump (R) Johnson (L) Spread …

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## Nevada data poll with near-ignorance priors and Python

I 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 …

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## A description of a Bayesian near-ignorance model for USA election polls

Election Poll for a single state In this and follwoing posts, I’ll present a way to compute Bayesian prediction for the result of USA 2016 election based on election poll data and near-ignorance prior models. This model is described in detail here: A. Benavoli and M. Zaffalon. “Prior near ignorance …

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## Battle for White House 2012

Battle for White House 2012 – 2 weeks before election The statistical analysis has been performed by using the most recent (2 weeks before election) polling data from realclearpolitics. The dataset can be downloaded here, while Matlab code can be downloaded here. The minimum sample size is around 500 people. …

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