Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Machine learning: A Bayesian and optimization perspective, 2nd edition, gives a unifying perspective on machine learning by covering both pillars of supervised learning, namely, regression and classification. The book starts with the basics, including mean-square, least-squares, and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. Then it moves on to more recent techniques, with emphasis on sparse modeling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models, and particle filtering. Dimensionality reduction and latent variables modeling are also considered in depth. The palette of techniques is concluded with an extended chapter on neural networks and deep learning architectures. The book also pays tribute to and covers fundamentals on statistical parameter estimation, Wiener and Kalman filtering, convexity, and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts..." -- from back cover.

Machine learning --- Bayesian statistical decision theory. --- Mathematical optimization. --- Mathematical models. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Learning, Machine --- Artificial intelligence --- Machine theory --- Apprentissage automatique --- Théorie de la décision bayésienne. --- Optimisation mathématique. --- Mathematics. --- Mathématiques.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Handbook of Computational Economics: Heterogeneous Agent Modeling, Volume Four, focuses on heterogeneous agent models, emphasizing recent advances in macroeconomics (including DSGE), finance, empirical validation and experiments, networks and related applications. Capturing the advances made since the publication of Volume Two (Tesfatsion & Judd, 2006), it provides high-level literature with sections devoted to Macroeconomics, Finance, Empirical Validation and Experiments, Networks, and other applications, including Innovation Diffusion in Heterogeneous Populations, Market Design and Electricity Markets, and a final section on Perspectives on Heterogeneity. Helps readers fully understand the dynamic properties of realistically rendered economic systems Emphasizes detailed specifications of structural conditions, institutional arrangements and behavioral dispositions Provides broad assessments that can lead researchers to recognize new synergies and opportunities

Economics, Mathematical --- Econometrics --- Equilibrium (Economics) --- Computer simulation --- 330.105 --- AA / International- internationaal --- 305.970 --- 330.3 --- 305.971 --- Wiskundige economie. Wiskundige methoden in de economie --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots. --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics. --- Speciale gevallen in econometrische modelbouw. --- 330.105 Wiskundige economie. Wiskundige methoden in de economie --- Planning (firm) --- Computer simulation. --- Mathématiques économiques --- Equilibre (Economie politique) --- Simulation par ordinateur --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Simulation methods --- Model-integrated computing --- Disequilibrium (Economics) --- Economic equilibrium --- General equilibrium (Economics) --- Partial equilibrium (Economics) --- Stagnation (Economics) --- Statics and dynamics (Social sciences) --- Mathematical economics --- E-books --- Mathématiques économiques --- 330.115 --- DGE (Economics) --- DSGE (Economics) --- Dynamic stochastic general equilibrium (Economics) --- SDGE (Economic theory) --- Economics --- Mathematics --- Statistics --- Mathematical models --- 330.115 Econometrie --- Econometrie --- Methodology --- Economics, Mathematical. --- Econometrics. --- Econométrie --- Algemeenheden: Autoregression and moving average representation. ARIMA. ARMAX. Lagrange multiplier. Wald. Function (mis) specification. Autocorrelation. Homoscedasticity. Heteroscedasticity. ARCH. GARCH. Integration and co-integration. Unit roots --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics --- Speciale gevallen in econometrische modelbouw --- Economics. --- Mathematical models. --- Equilibrium (physics)