From 1a6047414cd87952a4fac1a85db6606cd8d6ffb7 Mon Sep 17 00:00:00 2001 From: Will Handley Date: Thu, 22 Oct 2020 20:03:59 +0100 Subject: [PATCH] Added talk --- .gitignore | 9 + README.md | 18 + University_of_Cambridge.pdf | Bin 0 -> 60260 bytes cambridge_lecture.sty | 37 + cavendish.png | Bin 0 -> 111849 bytes figures/21cm.pdf | Bin 0 -> 558296 bytes figures/Colored_neural_network.pdf | Bin 0 -> 48797 bytes figures/H0.pdf | Bin 0 -> 72976 bytes figures/WMAP.png | Bin 0 -> 178765 bytes figures/chains.png | Bin 0 -> 113672 bytes figures/cobe.png | Bin 0 -> 157496 bytes figures/data.pdf | Bin 0 -> 83991 bytes figures/data_diff.pdf | Bin 0 -> 53569 bytes figures/data_diff_1.pdf | Bin 0 -> 49505 bytes figures/data_diff_2.pdf | Bin 0 -> 53574 bytes figures/data_points.pdf | Bin 0 -> 52119 bytes figures/dnest.pdf | Bin 0 -> 40313 bytes figures/dynesty.pdf | Bin 0 -> 206409 bytes figures/evidences.pdf | Bin 0 -> 135506 bytes figures/evidences_lin.pdf | Bin 0 -> 73605 bytes 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create mode 100644 kicc.png create mode 100644 talk.tex create mode 100644 will_handley_oxford_2020.pdf diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..209331c --- /dev/null +++ b/.gitignore @@ -0,0 +1,9 @@ +talk.pdf +*.aux +*.fdb_latexmk +*.fls +*.log +*.nav +*.out +*.snm +*.toc diff --git a/README.md b/README.md new file mode 100644 index 0000000..0f3b006 --- /dev/null +++ b/README.md @@ -0,0 +1,18 @@ +# Nested sampling: an efficient and robust Bayesian inference tool for astrophysics and cosmology. +hour-long seminar given on 21st January 2020 at the University of Oxford, UK + +## Abstract +Nested sampling is an alternative MCMC technique for integrating and exploring probability distributions. With +publicly available implementations such as MultiNest, PolyChord and dynesty, it has become widely adopted in the field +of cosmology and astrophysics as a powerful tool for computing Bayesian evidences and sampling challenging a-priori +unknown parameter spaces. + + In this talk I will give a user's guide to the theory of nested sampling in the context of Bayesian model comparison +and parameter estimation, + a survey of the current state of the art and the future of the field. I will illustrate with examples from +cosmological and inflationary model comparison, primordial power spectrum reconstruction, 21cm cosmology, exoplanets, +and tension quantification, as well as recent work in sparse reconstruction, Bayesian Neural Networks and +likelihood-free inference. + +[[PDF](https://github.com/williamjameshandley/talks/raw/oxford_2020/will_handley_oxford_2020.pdf)] + diff --git a/University_of_Cambridge.pdf b/University_of_Cambridge.pdf new file mode 100755 index 0000000000000000000000000000000000000000..ce9a73efddb186c837f16f7d61ed2d14a2049eb7 GIT binary patch literal 60260 zcmV(-K-|A2P((&8F)lO;CCBWKq6#%2Fd%PYY6?6&FHB`_XLM*FHXtw{QZGhnY;3i*^_?rugSmAZ(E{@<_XlfEAO&;Ne^{qtY{J-+_uKYyOD|M@fWXFa`q zl{P+4OJ9Hg&;dGQNGZ{iUDM z_DJgciS}2G&+2oJC(K3dU+vRJ+qd`qC$;@!ZQt;I@|1BKskKk)`?H_I*~UoH!@Sq* zSzdFV@7W``iG7P{Pvuui<@4-`%=wf1YF}%82L9c*HO9bfpS`Sf z-s|_Dx$n4fjp^NE{d@*i30FrQU-jXap4G#zn)Arte|p<@Sm(9k{$060fBw&(v{y9e 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