Hoff – A First Course in Bayesian Statistical Methods
Libro adottato a Bologna, a.a. 2026/2027 · 3 canali
«Hoff – A First Course in Bayesian Statistical Methods» è adottato per Inference dal prof. Silvia Cagnone (Economics and Finance, Scienze statistiche – Bologna); per Bayesian Inference dal prof. Daniela Cocchi (Statistical Sciences – Bologna).
Hoff – A First Course in Bayesian Statistical MethodsCerca su Amazon ›
Come lo indica il docente: Hoff P.D "A First Course in Bayesian Statistical Methods", Springer, 2009 · Hoff P. (2009) A First Course in Bayesian Statistical Methods, Springer
Titolo
A first course in bayesian statistical methods
Autori
Peter D. Hoff
Editore
Springer, 2010
ISBN
9781441928283
Chi lo adotta
Inference – Prof. Silvia Cagnone (canale unico)Economics and Finance · Laurea triennale (L-33) · esame facoltativo · 6 CFU
Hoff – A First Course in Bayesian Statistical Methodsquesto libroCerca su Amazon ›Verificato sulla scheda ufficiale il 02/10/2026
Bacheca del docente: cosa indica di studiare
Argomenti del programma: Introduction to the statistical inference. The Likelihood function. Sufficient statistics. Estimation theory . Moments and maximum likelihood estimation method. Point estimation: finite and asymptotic properties of estimators. Interval estimation: the pivotal quantity method and asymptotical confidence intervals. Hypothesis testing . Neyman-Pearson theory.
Hoff – A First Course in Bayesian Statistical Methodsquesto libroCerca su Amazon ›Verificato sulla scheda ufficiale il 02/10/2026
Bacheca del docente: cosa indica di studiare
Argomenti del programma: Introduction to the statistical inference. The Likelihood function. Sufficient statistics. Estimation theory . Moments and maximum likelihood estimation method. Point estimation: finite and asymptotic properties of estimators. Interval estimation: the pivotal quantity method and asymptotical confidence intervals. Hypothesis testing . Neyman-Pearson theory.
Argomenti del programma: Comparison between the classical and the Bayesian framework. Open problems in the classical statistical models. Bayes theorem for events and random variables. Revision of a prior via an experiment. Bayes inference for events with discrete priors. Odds ratios for couples of events. Posterior and predictive distributions for a Bernoulli likelihood and discrete and continuous prior distribution.
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