Likelihood and Bayesian signal processing methods for the analysis of auditory neural and behavioral data

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.Includes bibliographical references.Developing a consensus on how to model neural and behavioral responses and to quantify important response properties is a challenging signal processing problem because models do not always adequately capture the data and different methods often yield different estimates of the same response property. The threshold, the first stimulus level for which a difference between baseline activity and stimulus-driven activity exists, is an example of such a response property for both neural and behavioral responses.In the first and second sections of this work, we show how the state-space model framework can be used to represent neural and behavioral responses to auditory stimuli with a high degree of model goodness-of-fit. In the first section, we use likelihood methods to develop a state-space generalized linear model and estimate maximum likelihood parameters for neural data. In the second section, we develop the alternative Bayesian state-space model for behavioral data. Based on the estimated joint density, we then illustrate how important response properties, such as the neural and behavioral threshold, can be estimated, leading to lower threshold estimates than current methods by at least 2 dB. Our methods provide greater sensitivity, obviation of the hypothesis testing framework, and a more accurate description of the data.Formulating appropriate models to describe neural data in response to natural sound stimulation is another problem that currently represents a challenge. In the third section of the thesis, we develop a generalized linear model for responses to natural sound stimuli and estimate maximum likelihood parameters. Our methodology has the advantage of describing neural responses as point processes, capturing aspects of the stimulus response such as past spiking history and estimating the contributions of the various response covariates, resulting in a high degree of model goodness-of-fit.(cont) Using our model parameter estimates, we illustrate that decoding of the natural sound stimulus in our model framework produces neural discrimination performance on par with behavioral data.These findings have important implications for developing theoretically-sound and practical definitions of the neural response properties, for understanding information transmission within the auditory system and for design of auditory prostheses.by Anna A. Dreyer.Ph.D

Proceedings of the 36th International Workshop Statistical Modelling July 18-22, 2022 - Trieste, Italy

The 36th International Workshop on Statistical Modelling (IWSM) is the first one held in presence after a two year hiatus due to the COVID-19 pandemic. This edition was quite lively, with 60 oral presentations and 53 posters, covering a vast variety of topics. As usual, the extended abstracts of the papers are collected in the IWSM proceedings, but unlike the previous workshops, this year the proceedings will be not printed on paper, but it is only online. The workshop proudly maintains its almost unique feature of scheduling one plenary session for the whole week. This choice has always contributed to the stimulating atmosphere of the conference, combined with its informal character, encouraging the exchange of ideas and cross-fertilization among different areas as a distinguished tradition of the workshop, student participation has been strongly encouraged. This IWSM edition is particularly successful in this respect, as testified by the large number of students included in the program

Random Number Generators

The quasi-negative-binomial distribution was applied to queuing theory for determining the distribution of total number of customers served before the queue vanishes under certain assumptions. Some structural properties (probability generating function, convolution, mode and recurrence relation) for the moments of quasi-negative-binomial distribution are discussed. The distribution’s characterization and its relation with other distributions were investigated. A computer program was developed using R to obtain ML estimates and the distribution was fitted to some observed sets of data to test its goodness of fit

International Congress of Mathematicians: 2022 July 6–14: Proceedings of the ICM 2022

Following the long and illustrious tradition of the International Congress of Mathematicians, these proceedings include contributions based on the invited talks that were presented at the Congress in 2022. Published with the support of the International Mathematical Union and edited by Dmitry Beliaev and Stanislav Smirnov, these seven volumes present the most important developments in all fields of mathematics and its applications in the past four years. In particular, they include laudations and presentations of the 2022 Fields Medal winners and of the other prestigious prizes awarded at the Congress. The proceedings of the International Congress of Mathematicians provide an authoritative documentation of contemporary research in all branches of mathematics, and are an indispensable part of every mathematical library

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Statistical Analysis and Forecasting of Economic Structural Change

In 1984, the University of Bonn (FRG) and IIASA created a joint research group to analyze the relationship between economic growth and structural change. The research team was to examine the commodity composition as well as the size and direction of commodity and credit flows among countries and regions. Krelle (1988) reports on the results of this "Bonn-IIASA" research project. At the same time, an informal IIASA Working Group was initiated to deal with problems of the statistical analysis of economic data in the context of structural change: What tools do we have to identify nonconstancy of model parameters? What type of models are particularly applicable to nonconstant structure? How is forecasting affected by the presence of nonconstant structure? What problems should be anticipated in applying these tools and models? Some 50 experts, mainly statisticians or econometricians from about 15 countries, came together in Lodz, Poland (May 1985); Berlin, GDR (June 1986); and Sulejov, Poland (September 1986) to present and discuss their findings. This volume contains a selected set of those conference contributions as well as several specially invited chapters. The introductory chapter "What can statistics contribute to the analysis of economic structural change?", discusses not only the role of statistics in the detection and assimilation of structural changes, but also the relevance of respective methods in the evaluation of econometric models. Trends in the development of these methods are indicated, and the contributions to the present volume are put into a broader context of empirical economics to help to bridge the gap between economists and statisticians. The chapters in the first section are concerned with the detection of parameter nonconstancy. The procedures discussed range from classical methods, such as the CUSUM test, to new concepts, particularly those based on nonparametric statistics. Several chapters assess the conditions under which these methods can be applied and their robustness under such conditions. The second section addresses models that are in some sense generalizations of nonconstant-parameter models, so that they can assimilate structural changes. The last section deals with real-life structural change situations

Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations

The network structure (or topology) of a dynamical network is often unavailable or uncertain. Hence, we consider the problem of network reconstruction. Network reconstruction aims at inferring the topology of a dynamical network using measurements obtained from the network. In this technical note we define the notion of solvability of the network reconstruction problem. Subsequently, we provide necessary and sufficient conditions under which the network reconstruction problem is solvable. Finally, using constrained Lyapunov equations, we establish novel network reconstruction algorithms, applicable to general dynamical networks. We also provide specialized algorithms for specific network dynamics, such as the well-known consensus and adjacency dynamics.Comment: 8 page

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods