<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Max&#39;s Blog</title>
    <link>https://www.maxmantei.com/</link>
    <description>Recent content on Max&#39;s Blog</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en-us</language>
    <copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright>
    <lastBuildDate>Sat, 16 May 2020 00:00:00 +0000</lastBuildDate><atom:link href="https://www.maxmantei.com/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Installing CmdStanR on Windows</title>
      <link>https://www.maxmantei.com/2020/05/16/cmdstanr-windows/</link>
      <pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate>
      
      <guid>https://www.maxmantei.com/2020/05/16/cmdstanr-windows/</guid>
      <description>In this post I will share a few tips and tricks about installing CmdStanR on Windows. I write these down to not forget them myself, but maybe someone will find them useful as well. This page might be subject to change. It is not an official installation guide.
Why CmdStanR Over the past few years I have done a lot of statistical modeling in Stan. I remember this great blog post by Wayne Folta, which finally pushed me to switch from lme4 and the likes to rstanarm and brms.</description>
    </item>
    
    <item>
      <title>Hello there!</title>
      <link>https://www.maxmantei.com/about/</link>
      <pubDate>Tue, 12 May 2020 00:00:00 +0000</pubDate>
      
      <guid>https://www.maxmantei.com/about/</guid>
      <description>My name is Max. I’m a quantitative social scientist passionate about hierarchical Bayesian modeling, especially in Stan.
I&amp;rsquo;ve always had an interest in politic and economic phenomena and how to make sense of them through models and data. I strongly believe that domain expertise through informed priors and quantification of uncertainty through probabilistic reasoning are pinnacle of statistical modeling. I love exploring data, building bespoke models, and visualizing key insights.</description>
    </item>
    
    <item>
      <title>Simulating the Bundesliga (Part 1)</title>
      <link>https://www.maxmantei.com/2020/05/07/sim-buli-1/</link>
      <pubDate>Thu, 07 May 2020 00:00:00 +0000</pubDate>
      
      <guid>https://www.maxmantei.com/2020/05/07/sim-buli-1/</guid>
      <description>The current Covid-19 crisis brought public life to an abrupt halt. The German Bundesliga paused after its 25th match day. The last match was played on the 11th of March: A so called Geisterspiel between local rivals Gladbach and Köln in an empty Borussia-Park. Without cheering crowds, this derby, which Glabach won 2-1, had a somewhat eerie atmosphere. Amidst all discussion about how and when (or even if) the season should continue, I thought I could just build a simple statistical model and simulate the rest of the season.</description>
    </item>
    
  </channel>
</rss>
