

![0_image_0.png](0_image_0.png)

Alarm **Leaving** θx|u

![0_image_7.png](0_image_7.png)

true **true** .88 true **false** .12 false true .001 false false .999

![0_image_1.png](0_image_1.png)

![0_image_3.png](0_image_3.png)

Fire **Smoke** θx|u

![0_image_2.png](0_image_2.png)

true **true** .9 true **false** .1 false **true** .01 false **false** .99 Fire Tampering **Alarm** θx|u true true **true** .5 true true **false** .5 true false **true** .99 true false **false** .01 false true **true** .85 false true **false** .15 false false true .**0001**
false false false .**9999**
Leaving **Report** θx|u

![0_image_6.png](0_image_6.png)

![0_image_8.png](0_image_8.png)

true **true** .75 true **false** .25 false **true** .01 false **false** .99 Tampering θx|u

![0_image_4.png](0_image_4.png)

![0_image_5.png](0_image_5.png)

true .02 false .98
network structure is developed. Next, parameters are estimated by non-experts using a combination of statistical data and qualitative influences available from textbook materials.

Finally, medical experts are brought in to evaluate the network and fine-tune its parameters.

One method of evaluation is to pose diagnostic scenarios to the network, and compare the results of such queries to those expected by the experts. For **example, given some set of**
symptoms e, and two potential diagnoses y and z**, the network may give us the conclusion** that Pr (y | e)/Pr (z | e**) = 2, while a domain expert may believe that the ratio should**
be no less than 4. Assuming that the network structure is correct, a central question is then: which network parameters should be changed to give us the correct ratio, and by how much?

To automate the task of identifying such parameter changes, **we have recently developed a belief network tool, called** SamIam **(Sensitivity Analysis, Modelling, Inference And**