Jean-Michel Marin – A Practical Approach to Computational Bayesian Statistics
Libro adottato a Politecnico di Milano, a.a. 2026/2027 · 3 canali
«Jean-Michel Marin – A Practical Approach to Computational Bayesian Statistics» è adottato per Bayesian Learning And Montecarlo Simulation dal prof. Federico Bassetti (Computer Science and Engineering, Electrical Engineering, High Performance Computing Engineering – Politecnico di Milano).
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Come lo indica il docente: Jean-Michel Marin, Christian Robert, Bayesian Core: A Practical Approach to Computational Bayesian Statistics, Springer, Anno edizione: 2007, ISBN: 978-0-387-38983-7
Jean-Michel Marin – A Practical Approach to Computational Bayesian Statisticsquesto libroCerca su Amazon ›Verificato sulla scheda ufficiale il 03/10/2026
Bacheca del docente: cosa indica di studiare
Argomenti del programma: Principles of Bayesian learning . Quantifying uncertainty using probabilities. Likelihood, prior and posterior distributions: Bayes ’ Theorem for learning from data. Bayesian estimation and hypothesis testing. Posterior mean and variance, maximum a posteriori (MAP) estimate, posterior intervals, prediction, Bayes factor. Basic models . Bayesian learning for proportions (Bernoulli model- Beta prior).
Jean-Michel Marin – A Practical Approach to Computational Bayesian Statisticsquesto libroCerca su Amazon ›Verificato sulla scheda ufficiale il 03/10/2026
Bacheca del docente: cosa indica di studiare
Argomenti del programma: Principles of Bayesian learning . Quantifying uncertainty using probabilities. Likelihood, prior and posterior distributions: Bayes ’ Theorem for learning from data. Bayesian estimation and hypothesis testing. Posterior mean and variance, maximum a posteriori (MAP) estimate, posterior intervals, prediction, Bayes factor. Basic models . Bayesian learning for proportions (Bernoulli model- Beta prior).
Jean-Michel Marin – A Practical Approach to Computational Bayesian Statisticsquesto libroCerca su Amazon ›Verificato sulla scheda ufficiale il 03/10/2026
Bacheca del docente: cosa indica di studiare
Argomenti del programma: Principles of Bayesian learning . Quantifying uncertainty using probabilities. Likelihood, prior and posterior distributions: Bayes ’ Theorem for learning from data. Bayesian estimation and hypothesis testing. Posterior mean and variance, maximum a posteriori (MAP) estimate, posterior intervals, prediction, Bayes factor. Basic models . Bayesian learning for proportions (Bernoulli model- Beta prior).
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