From 64f2de83ca775ce759135a8dbdc10c553108683e Mon Sep 17 00:00:00 2001 From: Anthony Lim Date: Thu, 8 Aug 2024 12:00:54 +0100 Subject: [PATCH] refs #92 doc updats --- docs/source/cf_methods.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/cf_methods.rst b/docs/source/cf_methods.rst index cd2415be..6bbfd97b 100644 --- a/docs/source/cf_methods.rst +++ b/docs/source/cf_methods.rst @@ -35,7 +35,7 @@ Essentially these methods will sample the PDF directly, allowing them to generat Bayesian model selection use Bayes theorm to calculate the probability, :math:`P` of the data :math:`D` given the model :math:`M` .. math:: - :label: int + :name: eq_int P(D|M) = \int_\Omega P(D| \underline{\theta}, M)P( \underline{\theta}|M)\mathrm{d\underline{\theta}}. @@ -44,7 +44,7 @@ where the :math:`\underline{\theta}` are the parameters and the integral is over quickBayes ---------- -The quickBayes method makes a series of assumptions to reduce :ref:`the full PDF evaluation ` to a single analytic equation. +The quickBayes method makes a series of assumptions to reduce :ref:`the full PDF evaluation ` to a single analytic equation. The full theory is discussed here. The key assumptions are: @@ -71,7 +71,7 @@ where :math:`P(D | M)` is the probability of the data given the model, :math:`P( The probability of the data will be the same for all models, so by taking a ratio the term can be removed .. math:: - :label: eq:odds + :label: odds O_{21} = \frac{P(M_2 | D)}{P(M_1 | D) = \frac{P(D | M_2)P(M_2)}{P(D | M_1)P(M_1)}