From b18dbf55c0e84cd14f7701df5b82384147605954 Mon Sep 17 00:00:00 2001 From: wkaiz Date: Fri, 1 Nov 2024 16:32:07 -0700 Subject: [PATCH] fixing images --- bayes-nets/approximate.md | 2 +- bayes-nets/d-separation.md | 6 +++--- bayes-nets/representation.md | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/bayes-nets/approximate.md b/bayes-nets/approximate.md index f1b3540..91ddc32 100644 --- a/bayes-nets/approximate.md +++ b/bayes-nets/approximate.md @@ -80,4 +80,4 @@ We will not prove this, but if we repeat this process enough times, our later sa The pseudocode for Gibbs Sampling is provided below. -![Gibbs Sampling](../assets/images/Gibbs.png) \ No newline at end of file +Gibbs Sampling \ No newline at end of file diff --git a/bayes-nets/d-separation.md b/bayes-nets/d-separation.md index ba548fe..f8c1a99 100644 --- a/bayes-nets/d-separation.md +++ b/bayes-nets/d-separation.md @@ -17,10 +17,10 @@ We will present all three canonical cases of connected three-node two-edge Bayes ## 6.4.1 Causal Chains -![Causal Chain with no observations](../assets/images/chain_free.PNG) +Causal Chain with no observations *Figure 1: Causal Chain with no observations.* -![Causal Chain with Y observed](../assets/images/chain_observed.PNG) +Causal Chain with Y observed *Figure 2: Causal Chain with Y observed.* Figure 1 is a configuration of three nodes known as a **causal chain**. It expresses the following representation of the joint distribution over $$X$$, $$Y$$, and $$Z$$: @@ -178,7 +178,7 @@ Any path in a graph from $$X$$ to $$Y$$ can be decomposed into a set of 3 consec Here are some examples of applying the $$d$$-separation algorithm: -![Example 1](../assets/images/rbtt.png) +Example 1 This graph contains the common effect and causal chain canonical graphs. diff --git a/bayes-nets/representation.md b/bayes-nets/representation.md index a047754..749cb6b 100644 --- a/bayes-nets/representation.md +++ b/bayes-nets/representation.md @@ -35,7 +35,7 @@ As an example of a Bayes Net, consider a model where we have five binary random Assume the alarm can go off if either a burglary or an earthquake occurs, and that Mary and John will call if they hear the alarm. We can represent these dependencies with the graph shown below. -![Basic Bayes Nets Example](../assets/images/basic_bayes_nets.png) +Basic Bayes Nets Example

In this Bayes Net, we would store probability tables $$P(B)$$, $$P(E)$$, $$P(A | B, E)$$, $$P(J | A)$$ and $$P(M | A)$$.