{"id":7531,"date":"2017-11-20T00:00:00","date_gmt":"2017-11-19T23:00:00","guid":{"rendered":"https:\/\/www.ieec.cat\/iccub-course-an-introduction-to-bayesian-statistics-through-astronomical-application\/"},"modified":"2023-01-17T15:10:29","modified_gmt":"2023-01-17T14:10:29","slug":"iccub-course-an-introduction-to-bayesian-statistics-through-astronomical-application","status":"publish","type":"post","link":"https:\/\/www.ieec.cat\/en\/iccub-course-an-introduction-to-bayesian-statistics-through-astronomical-application\/","title":{"rendered":"ICCUB COURSE \u2013 An introduction to Bayesian Statistics through Astronomical Application"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; fullwidth=&#8221;on&#8221; _builder_version=&#8221;4.14.8&#8243; _module_preset=&#8221;default&#8221; background_image=&#8221;\/wp-content\/uploads\/2022\/04\/slider-corpo2.jpg&#8221; height=&#8221;114px&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; 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_builder_version=&#8221;4.14.8&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;0px||||false|false&#8221; custom_padding=&#8221;0px||||false|false&#8221; top_divider_color=&#8221;RGBA(255,255,255,0)&#8221; top_divider_height=&#8221;0px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.14.8&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.14.8&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/www.ieec.cat\/wp-content\/uploads\/2022\/12\/1545061534_552d139db7d4758e7e7d6ba12096dcbd_logoiccub.jpg&#8221; alt=&#8221;ICCUB COURSE \u2013 An introduction to Bayesian Statistics through Astronomical Application&#8221; title_text=&#8221;ICCUB COURSE \u2013 An introduction to Bayesian Statistics through Astronomical Application&#8221; _builder_version=&#8221;4.14.8&#8243; _module_preset=&#8221;default&#8221; width=&#8221;600px&#8221; module_alignment=&#8221;center&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_image][et_pb_text _builder_version=&#8221;4.14.8&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]The goal of this course is to provide undergraduate and masters students a first contact with Bayesian Inference. We will introduce basic probability concepts emphasizing the interpretation of probability in the Bayesian framework.<\/p>\n<p>We will use simple astronomical applications to illustrate applications of different probablity distributions, the use of nuisance parameters and marginalization, computation of confidence intervals, coordinate transformations.<\/p>\n<p>Schedule: 20, 21 and 22 of November from 17:40 to 19:00<\/p>\n<p><strong>Programme:<\/strong><\/p>\n<ul>\n<li>Lecture 1<\/li>\n<\/ul>\n<p>The concept of probability in Bayesian Statistics. The Sum and Product Rules. Bayes&rsquo;s Theorem.<br \/>\nExample: Least Squares Minimization &#8211; Chi2.<br \/>\nThe Coin Example (the effect of sample size and varying priors).<\/p>\n<ul>\n<li>Lecture 2<\/li>\n<\/ul>\n<p>Nuisance parameters and Marginalization.<br \/>\nExample: An Astrophysical application of the Coin Example Confidence Intervals. Coordinate Transformations.<br \/>\nExamples: Derivation of the &lsquo;Sum in Quadrature&rsquo; rule,computation of confidence intervals in N-dimensions.<\/p>\n<ul>\n<li>Lecture 3<\/li>\n<\/ul>\n<p>Probability distributions: example applications. Fitting a density modelto data (Poisson).<br \/>\nInference with many parameters: the &ldquo;astrophysical coin&rdquo; example using Markov Chain Monte Carlo.<\/p>\n<p>The course is open to all pre- and postdoc students<\/p>\n<p><strong>Contact email: <\/strong>tantoja(a)fqa.ub.edu[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The goal of this course is to provide undergraduate and masters students a first contact with Bayesian Inference. We will introduce basic probability concepts emphasizing the interpretation of probability in the Bayesian framework. 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