Lower-bounds on the Bayesian Risk in Estimation Procedures via ff-Divergences

We consider the problem of parameter estimation in a Bayesian setting and propose a general lower-bound that includes part of the family of $f$-Divergences. The results are then applied to specific settings of interest and compared to other notable results in the literature. In particular, we show that the known bounds using Mutual Information can be improved by using, for example, Maximal Leakage, Hellinger divergence, or generalizations of the Hockey-Stick divergence.Comment: Submitted to ISIT 202

Lower Bounds on the Bayesian Risk via Information Measures

This paper focuses on parameter estimation and introduces a new method for lower bounding the Bayesian risk. The method allows for the use of virtually \emph{any} information measure, including R\'enyi's $\alpha$, $\varphi$-Divergences, and Sibson's $\alpha$-Mutual Information. The approach considers divergences as functionals of measures and exploits the duality between spaces of measures and spaces of functions. In particular, we show that one can lower bound the risk with any information measure by upper bounding its dual via Markov's inequality. We are thus able to provide estimator-independent impossibility results thanks to the Data-Processing Inequalities that divergences satisfy. The results are then applied to settings of interest involving both discrete and continuous parameters, including the ``Hide-and-Seek'' problem, and compared to the state-of-the-art techniques. An important observation is that the behaviour of the lower bound in the number of samples is influenced by the choice of the information measure. We leverage this by introducing a new divergence inspired by the ``Hockey-Stick'' Divergence, which is demonstrated empirically to provide the largest lower-bound across all considered settings. If the observations are subject to privatisation, stronger impossibility results can be obtained via Strong Data-Processing Inequalities. The paper also discusses some generalisations and alternative directions

Decomposition Algorithms for Analyzing Transient Phenomena in Multi-class Queueing Networks in Air Transportation

In a previous paper (Peterson, Bertsimas, and Odoni 1992), we studied the phenomenon of transient congestion in landings at a hub airport and developed a recursive approach for computing moments of queue lengths and waiting times. In this paper we extend our approach to a network, developing two approximations based on the method used for the single hub. We present computational results for a simple 2-hub network and indicate the usefulness of the approach in analyzing the interaction between hubs. Although our motivation is drawn from air transportation, our method is applicable to all multi-class queuing networks where service capacity at a station may be modeled as a Markov or semi-Markov process. Our method represents a new approach for analyzing transient congestion phenomena in such networks. Airport congestion and delay have grown significantly over the last decade. By 1986 ground delays at domestic airports averaged 2000 hours per day, the equivalent of grounding the entire fleet of Delta Airlines at that tillie (250 aircraft) for one day (Donoghue 1986). In 1990, 21 airports in the U.S. exceeded 20, 000 hours of delay, with 12 more projected to exceed this total by 1997 (National Transportation Research Board 1991). This amounts to *School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana tSloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts ;Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusett