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I am wondering how the probabilities of the observation nodes in dynamic Bayesian networks are set.

I want to know whether the probabilities are monitored or are given by sensors?

So, what does the term observation nodes actually mean, and from what can I compute their probabilities?

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This question is rather unclear. Are you asking how the observed nodes in a Bayesian network affect the probability of the unobserved nodes? How to model the values for your specific problem (i.e. what distributions to use)? Clarifying your question will greatly increase our ability to provide a relevant answer. – Nick May 23 '12 at 16:32
i want to know what means observation node and it's value of probability is given from sensors? like in the example page 281 the Grey nodes are the evidence and at each time slice the robot observe a new evidence please help me.(cs.technion.ac.il/~dang/books/…) Thanks – WOW May 23 '12 at 18:36
observed nodes in a BN are nodes for which we have seen the actual values (e.g. from sensor data). The value has a likelihood given the node parameters. For instance, if the observed value from a sensor is assumed to have Gaussian noise about the true value and our current estimate of the true value is $x_t$, then the observed sensor value: $z_t$ has a likelihood of: $\mathcal{N}(z_t; x_t, \sigma)$. – Nick May 23 '12 at 20:45
Thank you very much for your answer. – WOW May 23 '12 at 23:12

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