From cbab9815cb77ce26d26747fba56675c2ef9713a3 Mon Sep 17 00:00:00 2001 From: mmcky Date: Mon, 19 Feb 2024 16:16:33 +1100 Subject: [PATCH] MAINT: migrate toms edits in 48354de46ee623cc602a0db9bd23b7aec5029a9c --- lectures/prob_meaning.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/lectures/prob_meaning.md b/lectures/prob_meaning.md index 8753525..dfde218 100644 --- a/lectures/prob_meaning.md +++ b/lectures/prob_meaning.md @@ -710,15 +710,13 @@ Typically, the functional form of the likelihood function determines the functio A natural question to ask is why should a person's personal prior about a parameter $\theta$ be restricted to be described by a conjugate prior? -Why not some other functional form that more sincerely describes the person's beliefs. +Why not some other functional form that more sincerely describes the person's beliefs? -To be argumentative, one could ask, why should the form of the likelihood function have *anything* to say about my -personal beliefs about $\theta$? +To be argumentative, one could ask, why should the form of the likelihood function have *anything* to say about my personal beliefs about $\theta$? A dignified response to that question is, well, it shouldn't, but if you want to compute a posterior easily you'll just be happier if your prior is conjugate to your likelihood. -Otherwise, your posterior won't have a convenient analytical form and you'll be in the situation of wanting to -apply the Markov chain Monte Carlo techniques deployed in {doc}`this quantecon lecture `. +Otherwise, your posterior won't have a convenient analytical form and you'll be in the situation of wanting to apply the Markov chain Monte Carlo techniques deployed in {doc}`this quantecon lecture `. We also apply these powerful methods to approximating Bayesian posteriors for non-conjugate priors in {doc}`this quantecon lecture ` and {doc}`this quantecon lecture `