A year ago I posted an article that visualised with word clouds subjects touched by the authors of this blog. The clouds contained stemmed and filtered nouns and verbs used in posts for each author that had produced at least 3 articles. Giles had suggested to take up the argument again the following year for a comparison, so here it is. Continue reading “Summarising blog content”
Yesterday, October 20, was the international day of Statistics. I took inspiration from it to select a clip from chapter 7 of my book “Anomaly! Collider physics and the quest for new phenomena at Fermilab“ which attempts to explain how physicists use the concept of statistical significance to give a quantitative meaning to their measurements of new effects. I hope you will enjoy it….
As we near the discussion of the discovery of the top quark, we need to make a digression to explain an important concept used by particle physicists to measure the level of surprise of an observation, i.e., how much are data at odds with a hypothesis. In a nutshell, the statistical significance of an observed Continue reading “What is Statistical Significance?”
So, it’s been a while since my last post, apologies for that, but the summer has been both busy and eventful, so let me summarise what’s been happening. Continue reading “Summer activities at LIP-Lisbon”
On Friday, September 8 th I attended a Sino-Italian workshop on astrostatistics organized at the Department of Statistical Sciences in Padova. It touched current topics at the interface between Astronomy, Physics and Statistics. At a first glance, I was surprised by the similarity of the research topics that are faced across different fields of science. Often the main difference lays only in the data and the assumptions of the underlying data generating process. Continue reading “Astro@stats Workshop”
On September 8, 2017 the Department of Statistical Sciences of the University of Padova will host Astro@Stats 2017, a Sino-Italian Workshop on Astrostatistics. Continue reading “Invitation to Astro@Stats 2017”
On the 19th of May I was very glad to take part in the RooStats tutorial organised by the AMVA4NewPhysics Network as a part of a workshop in Oviedo. RooStats is a ROOT library that uses the “RooFit” package, and provides classes to perform statistical analysis. The tutorial was attended by all the ESR from our Network, among which I was the only non-physicist. I am a statistician who does not use ROOT at all. For this reason, my attendance at the tutorial could seem Continue reading “My impressions on the RooStats Tutorial”
Just in time for our 4th all-network workshop, the AMVA4NewPhysics ITN reached full capacity with the addition of the two final ESRs, Seng (Munich) and Ioanna (Athens). The aforementioned workshop was hosted by the University of Oviedo, itself a recent addition to network, and organised by our outreach officer, Pietro, who did Continue reading “AMVA4NewPhysics: Fully-Armed and Operational!”
AMVA4NewPhysics has a little brother, INSIGHTS. The latter is in fact young but not so little, as it starts big: it is a new ITN that has just been approved and will be funded for four years with 3.14 million euros, starting in September 2017. Like AMVA4NewPhysics, INSIGHTS is a child of the marriage of particle physics and statistics, and like AMVA4NewPhysics it will train highly-skilled graduate students on a wide range of topics, bringing interdisciplinarity into their genes. Finally, like its older brother, INSIGHTS has INFN as a beneficiary partner, and it will bring funding to its Naples and Padova section, in the latter case with Dorigo, who is also the scientific coordinator of AMVA4NewPhysics, as PI.
It is important to recall that the whole concept of the ITN institution, one of the “Marie-Curie Actions” of the Research Executive Continue reading “INSIGHTS: a New Little Brother for AMVA4NewPhysics”
Last time we looked at how we can could fix some of the problems that were responsible for limiting the size of networks we could train. Here we will be covering some additions we can make to the models in order to further increase their power. Having learnt how to build powerful networks, we will also look into why exactly neural-networks can be so much more powerful than other methods.
Continue reading “Understanding Neural-Networks: Part IV – Improvements & Advantages”