Lecture 1: Introduction to random variables, common probability distributions<\/a> Lecture 4: Introduction to Bayesian statistics: Bayes theorem, prior choices<\/a> Lecture 7: Stochastic processes, optimal filtering, signal-to-noise ratio, sensitivity curves<\/a> Lecture 10: Time series analysis \u2013 auto-regressive process, moving average processes, ARMA models etc.<\/a> Week 1 & 2: Frequentist statistics Lecture 1: Introduction to random variables, common probability distributionsLecture 2: Basis statistical theory, including Cramer-Rao boundLecture 3: Hypothesis testing, Neyman-Pearson lemma, ROC curvesPractical 1: Introduction to R Week 3 & 4: Bayesian statistics Lecture… Continue Reading →<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-49","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/pages\/49","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/comments?post=49"}],"version-history":[{"count":2,"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/pages\/49\/revisions"}],"predecessor-version":[{"id":88,"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/pages\/49\/revisions\/88"}],"wp:attachment":[{"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/media?parent=49"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/categories?post=49"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imprs-lectures.aei.mpg.de\/potsdam-2019\/wp-json\/wp\/v2\/tags?post=49"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Lecture 2: Basis statistical theory, including Cramer-Rao bound<\/a>
Lecture 3: Hypothesis testing, Neyman-Pearson lemma, ROC curves<\/a>
Practical 1: Introduction to R<\/a><\/p>\n\n\n\nWeek 3 & 4: Bayesian statistics<\/h3>\n\n\n\n
Lecture 5: Introduction to Bayesian statistics: Bayesian hypothesis testing, posterior predictive checking, hierarchical models<\/a>
Lecture 6: Bayesian sampling methods<\/a>
Practical 2: Introduction to JAGS<\/a><\/p>\n\n\n\nWeek 5 & 6: Statistics in GW astronomy<\/h3>\n\n\n\n
Lecture 8: Frequentist statistics in GW astronomy \u2013 FAR, Fisher Matrix, PSD estimation<\/a>
Lecture 9: Bayesian statistics in GW astronomy \u2013 PE, population inference<\/a>
Practical 3: GW population analysis<\/a><\/p>\n\n\n\nWeek 7 & 8: Advanced topics<\/h3>\n\n\n\n
Lecture 11: Nonparametric regression \u2013 kernel density estimation, smoothing splines, wavelets<\/a>
Lecture 12: Gaussian processes, Dirichlet processes<\/a>
Practical 4: Nonparametric curve fitting<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"