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AMVA4NewPhysics

A Marie Sklodowska-Curie ITN funded by the Horizon2020 program of the European Commission

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data modelling

Convolutional Neural Networks and neutrinos

by Cecilia Tosciri

Have you ever wondered how Facebook suggests the tags for the picture you post on your wall, or how the photo library on your computer manages to automatically create albums containing pictures of particular people? Well, they use facial recognition software based on Convolutional Neural Network (CNN).

CNN is the most popular and effective method for object recognition, and it is a specialized kind of neural network for processing data that has a known grid-like topology. The network employs a mathematical operation Continue reading “Convolutional Neural Networks and neutrinos”

Analysis of Italian Roads

by Greg Kotkowski

It is said that “all roads lead to Rome”. Is it true anymore? Certainly, during the Roman Empire main roads were constructed in such the way that everybody could easily reach the capital, the political and economical center of the country. Therefore if roads are built in order to facilitate the transportation toward the most important hubs, they could be used as an indicator of a region’s importance.

I downloaded the data of all contemporary roads in Italy from the OSM. As a starter, it is worth to plot them all (see Figure 1). It is Continue reading “Analysis of Italian Roads”

Do Not Name Him Donald!

by Grzegorz Kotkowski

Recently I’ve encountered an interesting article about the trends of the female names in the US. It shows the impact of the famous Disney Movies on the names that are given to the newborns. As the “Frozen” movie has become very popular a lot of girls born in 2014 got names as Elsa or  Merida.

I want to consider the same dataset in order to perform the analogous analysis but for names of the US presidents. My guess is that it should well represent if a Continue reading “Do Not Name Him Donald!”

Smoothing alien data

by Pablo de Castro

What if you have some data you want to model, but do not know anything about its parent distribution, so you have to make as little assumptions as possible? In this post, I will go through the concept of density estimation and I will play with some interesting non-parametric methods.

Continue reading “Smoothing alien data”

Secondment in Padua

by Cecilia Tosciri

A few days ago I left Padua, where I spent one intensive month, working with other network members and ESR fellows (Giles, Greg and Pablo) at the Statistical Department.

A first observation

When people from different disciplines work together, like in the case of physicists and statisticians, the first stumbling block is the communication. This is mostly because Continue reading “Secondment in Padua”

Mixture of normals

by Greg Kotkowski

Modern statistical modelling seeks for more and more flexible methods to describe a wide variety of random phenomena. The Gaussian distribution is heavily exploited thanks to its properties, easy interpretation and simplicity. However, the data is often more complex and fitting it with a normal distribution is insufficient for skewed or heavily-tailed settings. Hence, more sophisticated methods are of great importance.

On the other hand, more complex approaches bring new difficulties. It is often not as straightforward Continue reading “Mixture of normals”

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