Caro gruppo Random, di sotto annuncio un seminario per il 1 Marzo. Saluti. *********************************************** You are welcome to the next Statistics seminar at Dept of Mathematical Sciences at Chalmers and Göteborg University. We are very glad to have, on Tuesday March 1, Maria Skoularidou <https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/maria-skoularidou/> who will talk of: ///"Estimating the directing information and testing for causality" / * * *About the speaker: *Maria Skoularidou <https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/maria-skoularidou/> is a PhD student at Cambridge University, UK. Her thesis is focused on probabilistic machine learning and, more precisely, towards using generative modelling in healthcare. Her fields of interest lie in the underpinnings, theory and methodology of machine intelligence, Bayesian inference, theoretical computer science and information theory. She is founder and chair of {Dis}Ability in AI <https://elesa.github.io/ability_in_AI/> and founder and secretary of Women in Data Science and Statistics (RSS special interest group). // //* * *Abstract: *The problem of estimating the directed information rate between two discrete processes { X n } and {Yn } via the plug-in (or maximum-likelihood) estimator is considered. When the joint process {(Xn,Yn)} is a Markov chain of a given memory length, the plug-in estimator is shown to be asymptotically Gaussian and to converge at the optimal rate O(1/√n) under appropriate conditions; this is the first estimator that has been shown to achieve this rate. An important connection is drawn between the problem of estimating the directed information rate and that of performing a hypothesis test for the presence of causal influence between the two processes. Under fairly general conditions, the null hypothesis, which corresponds to the absence of causal influence, is equivalent to the requirement that the directed information rate be equal to zero. In that case, a finer result is established, showing that the plug-in converges at the faster rate O (1/ n) and that it is asymptotically χ 2 -distributed. This is proved by showing that this estimator is equal to (a scalar multiple of) the classical likelihood ratio statistic for the above hypothesis test. Finally, it is noted that these results facilitate the design of an actual likelihood ratio test for the presence or absence of causal influence.* * *Feel free to spread this announcement in your network.* * * *Where*:https://chalmers.zoom.us/j/61015800942 Password: 697666 ** **** *When*: Tuesday 1 March at 14.15-15.15*(Swedish time)*. ** -- _________________________________________________________ Umberto Picchini, Associate Professor, PhD, Docent https://umbertopicchini.github.io/ , twitter: @uPicchini