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With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing.

Language and languages --- Bayesian statistical decision theory. --- Study and teaching --- Statistical method.

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Mathematical statistics --- Robust statistics --- Méthode statistique --- Statistical methods --- 681.3*G3 --- 519.22 --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Statistical theory. Statistical models. Mathematical statistics in general --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Robust statistics. --- Statistiques robustes --- Robust statistical method --- Quasi-robustness --- Robustesse

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The Life cycle cost (LCC) method makes it possible for the whole life performance of buildings and other structures to be optimized. The introduction of the idea of thinking in terms of a building life cycle resulted in the need to use appropriate tools and techniques for assessing and analyzing costs throughout the life cycle of the building. Traditionally, estimates of LCC have been calculated based on historical analysis of data and have used deterministic models. The concepts of probability theory can also be applied to life cycle costing, treating the costs and timings as a stochastic process. If any subjectivity is introduced into the estimates, then the uncertainty cannot be handled using the probability theory alone. The theory of fuzzy sets is a valuable tool for handling such uncertainties. In this Special Issue, a collection of 11 contributions provide an updated overview of the approaches for estimating the life cycle cost of buildings.

Technology: general issues --- dynamic analysis --- steel frames --- Tuned Mass Damper --- optimization --- drift ratio --- sustainable construction industry --- lifecycles --- European Union Member States --- complex evaluation --- multiple criteria analysis --- COPRAS and INVAR methods --- success and image of a country --- marketing --- residential buildings --- defects --- intensity --- reliability --- technical wear --- railway infrastructure --- occurrences --- socioeconomic impact --- economic evaluation --- CBA --- life cycle --- investment project --- probability distribution --- sensitivity analyses --- risk assessment --- tenement houses --- damage --- maintenance --- fuzzy sets --- Bayes conditional probability --- substitution --- operation and maintenance phase --- cause–effect relationships --- historical buildings --- implementation factors --- information and communication technology --- life cycle costs --- buildings --- bidding decision --- LCC criterion --- price criterion --- construction --- statistical method --- classification --- probability of winning --- risk identification --- MCDM --- critical risk factors --- commercial and recreational complex building projects

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Some in the social sciences argue that the same logic applies to both qualitative and quantitative methods. In A Tale of Two Cultures, Gary Goertz and James Mahoney demonstrate that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. They identify and discuss major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, Goertz and Mahoney also seek to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. This book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Social sciences --- Political sociology --- Political science --- Mass political behavior --- Political behavior --- Sociology --- Administration --- Civil government --- Commonwealth, The --- Government --- Political theory --- Political thought --- Politics --- Science, Political --- State, The --- Research --- Methodology. --- Sociological aspects --- 2 x 2 tables. --- David Hume. --- Fundamental Principle of Variable Transformation. --- Fundamental Problem of Causal Inference. --- Fundamental Tradeoffs. --- Hooke's law. --- Principle of Conceptual Opposites. --- Principle of Conceptual Overlap. --- Principle of Unimportant Variation. --- additive-linear causal model. --- aggregation technique. --- asymmetry. --- case selection. --- case studies. --- cases. --- categories. --- causal complexity. --- causal effects. --- causal heterogeneity. --- causal inference. --- causal mechanism. --- causal model. --- causal models. --- causal-process observations. --- causality. --- causation. --- cause. --- causes-of-effects approach. --- characteristics. --- concepts. --- conceptualization. --- constant conjunction definition. --- control variables. --- counterfactual analysis. --- counterfactual definition. --- counterfactuals. --- cross-case analysis. --- data analysis. --- data transformations. --- data-set observations. --- definitions. --- dependent variable. --- effects-of-causes approach. --- empirical testing. --- equifinality. --- error. --- experiments. --- fuzziness. --- fuzzy-set analysis. --- fuzzy-set transformations. --- generalization. --- hypothesis testing. --- indicators. --- individual case analysis. --- individual cases. --- inferential statistics. --- logging. --- logic. --- meaning retention. --- measurement. --- membership functions. --- methodological pluralism. --- minimum rewrite rule. --- mixed-method research. --- multimethod research. --- multiple causation. --- natural language. --- necessary condition. --- nonoccurrence. --- occurrence. --- opposites. --- perfect predictors. --- political science. --- probability theory. --- process tracing. --- qualitative research. --- quantitative research. --- regression. --- scale types. --- scope conditions. --- semantic transformations. --- semantics. --- set theory. --- set-theoretic causal model. --- set-theoretic generalization. --- social science research. --- social sciences. --- sociology. --- standardization. --- static causal asymmetry. --- statistical analysis. --- statistical method. --- statistical model. --- statistics. --- sufficient condition. --- symmetry. --- translation problems. --- typologies. --- variable transformations. --- within-case analysis. --- within-model responses.

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This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators.

sensor network --- data fusion --- complex network analysis --- fault prognosis --- photovoltaic plants --- ANFIS --- statistical method --- gradient descent --- photovoltaic system --- sustainable development --- PV power prediction --- artificial neural network --- renewable energy --- environmental parameters --- multiple regression model --- moth-flame optimization --- parameter extraction --- photovoltaic model --- double flames generation (DFG) strategy --- Solar cell parameters --- single-diode model --- two-diode model --- COA --- photovoltaic systems --- maximum power point tracking --- single stage grid connected systems --- solar concentrator --- spectral beam splitting --- diffractive optical element --- diffractive grating --- PVs power output forecasting --- adaptive neuro-fuzzy inference systems --- particle swarm optimization-artificial neural networks --- solar irradiation --- photovoltaic power prediction --- publicly available weather reports --- machine learning --- long short-term memory --- integrated energy systems --- smart energy management --- PV fleet --- clustering-based PV fault detection --- unsupervised learning --- self-imputation --- implicit model solution --- photovoltaic array --- series–parallel --- global optimization --- partial shading --- deterministic optimization algorithm --- metaheuristic optimization algorithm --- genetic algorithm --- solar cell optimization --- finite difference time domain --- optical modelling --- thermal image --- photovoltaic module --- hot spot --- image processing --- deterioration --- linear approximation --- MPPT algorithm --- duty cycle --- global horizontal irradiance --- mathematical modeling --- feed-forward neural networks --- recurrent neural networks --- LSTM cell --- performances evaluation --- clear sky irradiance --- persistent predictor --- photovoltaics --- artificial neural networks --- national power system