Prof. Ben Allanach (DAMTP) will give a public lecture on particle physics to shed some light into what exactly the Higgs boson discovery means and what happens next.
About: DU Mathsoc and DU Physoc present a special event for Trinity's 4th week, coinciding with the CERN Exhibition visit to Dublin and Maths Week 2013. The event is open to the public, and content will be aimed at a general audience.
A professor of theoretical physics in Cambridge, Prof. Ben Allanach works on collider searches for new particles in conjunction with the Cambridge Supersymmetry Working Group. Known for his public engagement, Prof. Allanach has given several public lectures, written for various websites and given a talk at TEDxDanubia (http://www.youtube.com/watch?v=cZfw1XKkh6s).
From Prof. Allanach, as a brief description of the talk:
"I shall describe the dark matter mystery, and we shall go on a speculative journey to solve it. Going back to the beginning of time, we shall witness the birth of a proton, following it through to the present day, where it ends up in the Large Hadron Collider at CERN. We shall revisit the Higgs boson discovery from last year. Finally, we shall see show how the collisions between protons at CERN might give us vital clues to solve the dark matter mystery."
See our Facebook event here: https://www.facebook.com/events/729117993771326/
One of the most renowned statisticians in the world (the most highly cited man in mathematics for the decade 1995-2005), our alumnus Prof. Adrian Raftery has developed new statistical methods for social and health sciences, as well as advancing a number of new modeling methods for use in a variety of systems.
About: "Probabilistic weather forecasting consists of finding a joint
probability distribution for future weather quantities or events.
Information about the uncertainty of weather forecasts can be important for
decision-makers as well as the public, but currently is routinely provided
only for the probability of precipitation, and not for other weather
quantities such as temperature, wind or amount of precipitation. It is typically done using a numerical weather prediction model, perturbing the inputs to the model (initial conditions, physics parameters) in various ways, and running the model for each perturbed set of inputs. The result is viewed as an ensemble of forecasts, taken to be a sample from the joint probability distribution of the future weather quantities of interest. The results are often uncalibrated, however.
I will review a principled statistical method for postprocessing ensembles
based on Bayesian Model Averaging (BMA), that models the predictive
distribution conditionally on the ensemble by a finite mixture model.
I will describe applications to precipitation, wind speeds, wind directions,
visibility and winter road maintenance, a multivariate decision problem.
For probabilistic forecasting of an entire weather field,
I describe a spatial extension of the BMA method that
perturbs the outputs from the numerical weather prediction model
rather than the inputs. Forecasts are available in real time at
www.probcast.washington.edu, and the R packages ensembleBMA and ProbForecastGOP are available to implement the methods."