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README.md

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 # Cognitive Models with Julia
 
 [![](https://img.shields.io/badge/status-looking_for_collaborators-orange)](https://github.com/DominiqueMakowski/CognitiveModels/issues)
+[![](https://img.shields.io/badge/access-open-green)](https://dominiquemakowski.github.io/CognitiveModels/)
 
 The project is to write an open-access book on **cognitive models**, i.e., statistical models that best fit **psychological data** (e.g., reaction times, scales from surveys, ...). 
 This framework aims at moving away from a mere description of the data, to make inferences about the underlying cognitive processes that led to its generation.

content/.quarto/cites/index.json

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content/.quarto/idx/1_introduction.qmd.json

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+{"title":"Fundamentals of Bayesian Modeling in Julia","markdown":{"headingText":"Fundamentals of Bayesian Modeling in Julia","containsRefs":false,"markdown":"\n![](https://img.shields.io/badge/status-not_started-red)\n\n\n## Very quick intro to Julia and Turing\n\nGoal is to teach just enough so that the reader understands the code.\n\n### Generate Data from Normal Distribution\n\n```{julia}\nusing Turing, Distributions, Random\nusing Makie\n\n# μ=100, σ=15\niq = rand(Normal(100, 15), 500)\n```\n\n```{julia}\nfig = Figure()\nax = Axis(fig[1, 1], title=\"Distribution\")\ndensity!(ax, iq)\nfig\n```\n\n### Recover Distribution Parameters with Turing\n\n```{julia}\n@model function model_gaussian(x)\n    # Priors\n    μ ~ Uniform(0, 200)\n    σ ~ Uniform(0, 30)\n\n    # Check against data\n    for i in 1:length(x)\n        x[i] ~ Normal(μ, σ)\n    end\nend\n\nmodel = model_gaussian(iq)\nsampling_results = sample(model, NUTS(), 400)\n\n# Summary (95% CI)\nsummarystats(sampling_results)\n```\n\n\n## Linear Models\n\nUnderstand what the parameters mean (intercept, slopes, sigma).\n\n3. 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