From fa69d0261596ebc49ed1cb26292d4a0fa53d9a30 Mon Sep 17 00:00:00 2001 From: Lei Date: Thu, 22 Sep 2022 15:44:00 +0100 Subject: [PATCH] Update section1_introduction.jl --- section1_introduction.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/section1_introduction.jl b/section1_introduction.jl index a699657..7ebfa55 100644 --- a/section1_introduction.jl +++ b/section1_introduction.jl @@ -831,7 +831,7 @@ After finishing specifying the model, what is left is to routinely apply Bayes' Note the prior is a constant, and the posterior is proportional to the likelihood: -$$p(\theta_A, \theta_B|\mathcal D) \propto p(\theta_A, \theta_B)\cdot p(\mathcal D|\theta_A, \theta_B) = \frac{1}{11^2} \cdot p(\mathcal D|\theta_A, \theta_B).$$ +$$p(\theta_A, \theta_B|\mathcal D) \propto p(\theta_A, \theta_B)\cdot p(\mathcal D|\theta_A, \theta_B) = \frac{1}{101^2} \cdot p(\mathcal D|\theta_A, \theta_B).$$ And the normalising constant $p(\mathcal D)$ is