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1. A Bayesian network is shown for the variables paper Thickness, paper Alignment and Print Quality. The conditional probabilities are provided in the tables beside the nodes. Here, Thickness = {high or low}, Alignment = {straight or angled}, and Print Quality = {good or bad}.
Calculate the following quantities from the information in the Bayesian network.
a) P(Alignment = straight)
b) P(Alignment = straight | Print Quality = good)
These days, they're pretty much obsolete; they're definitely system-particular. They had to do with 16-bit programming under MS-DOS and perhaps some early on versions of Windows. I
Before 1994 there were dissimilar methodologies like Rumbaugh, Booch, Jacobson, and Meyer etc who followed their own notations to mould the systems. The developers were in a di
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its language base assignment
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