First, let us apply Bayes formula as usual, then we will see if we can identify that as operations on the probability tree:
$$\DeclareMathOperator{\P}{\mathbb{P}}
\P(\text{L} \mid \text{test R}) =\frac{\P(\text{test R}\mid \text{L})\P(L)}{\P(\text{test R})}
$$
Comparing this with the probability tree below, we see it involve all the nodes except the two "Tests as left-handed",
so we can redraw the tree without those nodes:
Then let us put the numbers into the Bayes formula above:
$$
\P(\text{L} \mid \text{test R}) =\frac{(0.1)\cdot(0)}{(0.1)\cdot (0) + (0.9)\cdot (0.95)} = 0
$$
Then observe that in numerator we have the (sum of) path probabilities that passes through the node "Actually left-handed" (denoted L
in the formulas here), while in the numerator we have the (sum of) all path probabilities that leads to one of the nodes "test as right-handed".
We can formulate that as a rule for applying Bayes theorem on a probability tree, for $\P(A \mid B)$, naming $A$ as 'cause' and $B$ as 'data':
- Eliminate all the paths through the tree made impossible by the conditioning on data $B$.
- Denominator is sum of all path-probabilities consistent with $B$
- Numerator is sum of all path-probabilities consistent with cause $A$
self-study
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