## 19 September Tutorial at ECML

Working on the slides for our Tutorial at ECML 2016 (Riva del Garda)   G. Corani, A. Benavoli, J. Demsar.  Comparing competing algorithms: Bayesian versus frequentist hypothesis testing Schedule Time Duration Content Details 09:00 15min Introduction Motivations and Goals 09:15 60min Null hypothesis significance tests in machine learning NHST testing …

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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 2016

Also for the 2016 USA election, I will periodically post election polls for the battle for White House using Bayesian methods based on near-ignorance prior probabilities that automatically allows to perform swing scenarios (e.g., a percentage of voters decide to change their vote). This is the first result with using …

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## Wikipedia

I have just completed the wikipedia page for the Imprecise Dirichlet process, https://en.wikipedia.org/wiki/Imprecise_Dirichlet_process …any useful contribution/modification is welcome.

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## Comparing climbing performance

This post shows how to use the IDP statistical package to compare sport performance.  As case study, I have considered (just for fun) the comparison of my climbing performance in two consecutive editions (2013 and 2014) of the “Tre Valli Bresciane” cycling race. The following table reports my ascent time …

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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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