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Algorithms of Power
The literature on "bridging the semantic gap" between mass and network mediated visuals and algorithms for their automatic identification and classification is growing and requires transdisciplinary contributions in Part I by eminent computer and social scientists. In Part II, scholars from the social sciences and journalism explore a few major landmarks of the vastly neglected and more challenging areas of soundscapes and multi-sensory experiences as well as censorship.
Information Theory, Inference and Learning Algorithms
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independ...
Cross-Layer Design in Optical Networks
This work addresses the topic of optical networks cross-layer design with a focus on physical-layer-impairment-aware design. Contributors captures both the physical-layer-aware network design as well as the latest advances in service-layer-aware network design. Treatment of topics such as, optical transmissions which are prone to signal impairments, dense packing of wavelengths, dispersion, crosstalk, etc., as well as how to design the network to mitigate such impairments, are all covered.
Probability and Algorithms
Some of the hardest computational problems have been successfully attacked through the use of probabilistic algorithms, which have an element of randomness to them. Concepts from the field of probability are also increasingly useful in analyzing the performance of algorithms, broadening our understanding beyond that provided by the worst-case or average-case analyses. This book surveys both of these emerging areas on the interface of the mathematical sciences and computer science. It is designed to attract new researchers to this area and provide them with enough background to begin explorations of their own.
The Econometrics of Complex Survey Data
This volume of Advances in Econometrics contains a selection of papers presented at the 'Econometrics of Complex Survey Data: Theory and Applications' conference organized by the Bank of Canada, Ottawa, Canada, from October 19-20, 2017.
Algorithms for Decision Making
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty...
Analysis of Panels and Limited Dependent Variable Models
This important collection brings together leading econometricians to discuss advances in the areas of the econometrics of panel data. The papers in this collection can be grouped into two categories. The first, which includes chapters by Amemiya, Baltagi, Arellano, Bover and Labeaga, primarily deal with different aspects of limited dependent variables and sample selectivity. The second group of papers, including those by Nerlove, Schmidt and Ahn, Kiviet, Davies and Lahiri, consider issues that arise in the estimation of dyanamic (possibly) heterogeneous panel data models. Overall, the contributors focus on the issues of simplifying complex real-world phenomena into easily generalisable inferences from individual outcomes. As the contributions of G. S. Maddala in the fields of limited dependent variables and panel data were particularly influential, it is a fitting tribute that this volume is dedicated to him.
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space. Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved. The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation.
