HANS J. SKAUG
Title: What exact derivatives can do for statisticians
Abstract: Exact numerical derivatives are currently powering the deep learning revolution in machine learning. The underlying algorithm is called backpropagation, and is an efficient way of calculating the gradient of the objective function with respect to the model parameters. The same algorithm can be applied repeatedly to obtain first and higher order derivatives of any computer program in an automatic manner, and is then often referred to as automatic differentiation (AD). I will give an overview of statistical methods that are well suited for AD. Among these are Laplace and saddlepoint approximations, modified profile likelihood, Hamiltonian Monte Carlo, Fisher information matrices. My main point is that AD makes these methods much easier accessible to a broad audience. I will show examples implemented in the software system TMB (https://github.com/kaskr/adcomp)
Download the flyer here.
MICHAEL SPAGAT
Title: On the decline of war
Abstract: For the past 70 years, there has been a downward trend in the size of wars, but the idea of an enduring ‘long peace’ remains controversial. Some recent contributions suggest that observed war patterns, including the long peace, could have come from a long-standing and unchanging war-generating process, an idea rooted in Lewis F Richardson’s pioneering work on war. Aaron Clauset has tested the hypothesis that the war sizes after the Second World War are generated by the same mechanism that generated war sizes before the Second World War and fails to reject the ‘no-change’ hypothesis. In this chapter, we transform the war-size data into units of battle deaths per 100,000 of world population rather than absolute battle deaths – units appropriate for investigating the probability that a random person will die in a war. This change tilts the evidence towards rejecting the no-change hypothesis. We also show that sliding the candidate break point slightly forward in time, to 1950 rather than 1945, leads us further down the path toward formal rejection of the no-change hypothesis. Finally, we expand the range of wars to include not just the inter-state wars considered by Clauset (2018) but also intra-state wars. Now we do formally reject the no-change hypothesis. Finally, we show that our results do not depend on the choice between two widely used war datasets.
Download the flyer here.