The diagram above is the Bayesian network of my problem. I want to find $$\Pr(B=F \mid E=F, A=T)$$ I have evaluated it into the following steps, then I got a bit stuck: $$\Pr(B=F \mid E=F, A=T) = \frac{\Pr(B=F,E=F,A=T)}{\Pr(E=F,A=T)}$$ $$=\frac{\Pr(B=F,E=F,A=T)}{\Pr(A=T\mid E=F)\times\Pr(E=F)}$$ $$$$ I was able to get $\Pr(B=F,E=F,A=T)$ from: $\Pr(B=F,E=F,A=T)=\Pr(A=T\mid B=F,E=F) \times \Pr(B=F,E=F)$ $\Pr(B=F,E=F,A=T)=\Pr(A=T\mid B=F,E=F) \times […]

I’m working on trying to implement a hidden markov model to model the affect of a specific protein that can cut an RNA when the ribosome is translating the RNA slowly. Some brief background: The ribosome translates mRNA into protein The ribosome can occasionally pause on the mRNA due to things such as secondary structure […]

It’s well known that 3 random variables may be pairwise statistically independent but not mutually independent, for an illustration see: example pairwise vs. mutual relations. Can mutual statistical independence be modeled with Bayesian Networks aka Graphical Models? These are nonparametric structured stochastic models encoded by Directed Acyclic Graphs. “Each vertex represents a random variable (or […]

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