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add lectures
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README.md

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Collection of various small write-ups on specific topics from the fields of:
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This is the repository of my blog. [See the rendered version of this repository here.](https://hdembinski.github.io/)
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- Statistics
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- Programming
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- Physics
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All notebooks are released under the MIT license, see the LICENSE file in the repository.
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Notebooks in this repository are released under the MIT license, see the LICENSE file in the repository.

_quarto.yml

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website:
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title: Hans Dembinski's blog
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navbar:
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left:
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- lectures.qmd
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right:
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- about.qmd
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- icon: linkedin

index.qmd

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---
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listing:
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contents:
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- "posts/*.ipynb"
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- "posts/*.md"
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contents: posts
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sort: "date desc"
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type: default
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categories: false

lectures.qmd

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---
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title: "Lectures"
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listing:
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contents: lectures
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type: default
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page-layout: full
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---

2024_LHCb_Starterkit/lecture_1.ipynb renamed to lectures/2024_LHCb_Starterkit/lecture_1.ipynb

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}
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},
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"source": [
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"# Statistics I\n",
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"# LHCb Starter Kit 2023: Statistics I\n",
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"\n",
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"**Hans Dembinski, TU Dortmund**\n",
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"\n",

2024_LHCb_Starterkit/lecture_1_full.ipynb renamed to lectures/2024_LHCb_Starterkit/lecture_1_full.ipynb

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}
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},
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"source": [
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"# Statistics I\n",
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"# LHCb Starter Kit 2023: Statistics I (extended version)\n",
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"\n",
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"**Hans Dembinski, TU Dortmund**\n",
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"\n",

2024_LHCb_Starterkit/lecture_2.ipynb renamed to lectures/2024_LHCb_Starterkit/lecture_2.ipynb

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}
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},
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"source": [
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"# Statistics II\n",
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"# LHCb Starter Kit 2023: Statistics II\n",
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"\n",
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"**Hans Dembinski, TU Dortmund**\n",
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"\n",

2024_LHCb_Starterkit/lecture_2_full.ipynb renamed to lectures/2024_LHCb_Starterkit/lecture_2_full.ipynb

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}
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},
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"source": [
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"# Statistics II\n",
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"# LHCb Starter Kit 2023: Statistics II (extended version)\n",
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"\n",
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"**Hans Dembinski, TU Dortmund**\n",
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"\n",
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lectures/_metadata.yml

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# options specified here will apply to all posts in this folder
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# freeze computational output
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# (see https://quarto.org/docs/projects/code-execution.html#freeze)
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freeze: true
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# Enable banner style title blocks
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title-block-banner: true

posts/cows.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Fitting mixture models with COWs\n",
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"# Fitting mixtures with COWs\n",
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"\n",
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"This is a demo of how to use custom orthogonal weight functions (COWs) to generate weights which extract one component of a mixture. Find out more [in our paper](https://inspirehep.net/literature/1986730)."
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]

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