<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Guide on Wise Lab Wiki</title><link>https://wiki.thewiselab.org/docs/computational_modelling/guide/</link><description>Recent content in Guide on Wise Lab Wiki</description><generator>Hugo</generator><language>en</language><copyright>Copyright (c) 2020-2024 Hyas</copyright><lastBuildDate>Thu, 07 Sep 2023 16:04:48 +0200</lastBuildDate><atom:link href="https://wiki.thewiselab.org/docs/computational_modelling/guide/index.xml" rel="self" type="application/rss+xml"/><item><title>1. Building basic models</title><link>https://wiki.thewiselab.org/docs/computational_modelling/guide/1.-building-basic-models/</link><pubDate>Thu, 07 Sep 2023 16:04:48 +0200</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/guide/1.-building-basic-models/</guid><description>This guide will take you through the process of building trial-by-trial learning models. It assumes some knowledge of how these models work, and is more focused on the implementation.</description></item><item><title>2. Modelling multiple participants</title><link>https://wiki.thewiselab.org/docs/computational_modelling/guide/2.-modelling-multiple-participants/</link><pubDate>Thu, 07 Sep 2023 16:04:48 +0200</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/guide/2.-modelling-multiple-participants/</guid><description>In the previous section, we discussed how to model a single participant. In this section, we will discuss how to model multiple participants.</description></item><item><title>3. MCMC sampling</title><link>https://wiki.thewiselab.org/docs/computational_modelling/guide/3.-mcmc-sampling/</link><pubDate>Thu, 07 Sep 2023 16:04:48 +0200</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/guide/3.-mcmc-sampling/</guid><description>The previous sections outline how to generate simulated data using a computational model. In this section, we will discuss how to use Markov Chain Monte Carlo (MCMC) sampling to fit a model to data.</description></item><item><title>4. Hierarchical models</title><link>https://wiki.thewiselab.org/docs/computational_modelling/guide/4.-hierarchical-models/</link><pubDate>Thu, 07 Sep 2023 16:04:48 +0200</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/guide/4.-hierarchical-models/</guid><description>The statistical model described in the previous section assumes that participants are independent of one another. In reality, participants are drawn from a group that will often have shared characteristics.</description></item><item><title>5. Visualising MCMC results</title><link>https://wiki.thewiselab.org/docs/computational_modelling/guide/5.-visualising-mcmc-results/</link><pubDate>Thu, 07 Sep 2023 16:04:48 +0200</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/guide/5.-visualising-mcmc-results/</guid><description>The best way to visualise and check the results when using sampling is to use the ArviZ library. This library is designed to work with a range of sampling packages, including NumPyro.</description></item><item><title>6. Simulation based inference</title><link>https://wiki.thewiselab.org/docs/computational_modelling/guide/6.-simulation-based-inference/</link><pubDate>Thu, 07 Sep 2023 16:04:48 +0200</pubDate><guid>https://wiki.thewiselab.org/docs/computational_modelling/guide/6.-simulation-based-inference/</guid><description>Coming soon&amp;hellip;</description></item></channel></rss>