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Because each tree is assumed to be independent (by definition of independent ancestry), the complete probability of the data assuming an independent ancestry model is simply the product of the probabilities of the several trees that compose it.
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Even if the event history matrix A is completely known, the question remains as to whether π is identifiable, i.e. does complete knowledge of the probability distribution of the data, q, allow for reconstruction of the parameter π?
Given model parameters Φ, the probability of the complete data can be expressed as p ( x, s | Φ ) = w 0, i ∏ t = 1 T - 1 w s t, s t + 1 ∏ t = 1 T p ( x t | θ s t ) (5).
It's a complete misrepresentation of the data.
where P n, i (T) is the estimated probability of successfully completing delivery of the media data unit n in discrete time interval T on channel i, NT is the target chunk delivery latency and t n, i * is the optimal waiting threshold (timeout in terms of intervals T) for receipt of the media chunk before triggering a switch to channel i.
28 32 Inverse probability weighting (IPW) is used to make a valid complete case analysis under MAR by weighting complete cases with the inverse of their probability of having data observed.
We further prove that the probability of the complete synchronization converges exponentially fast.
This measure indicates the ratio of the probabilities of finding an active compound in the first X% of the data set and that of sampling one at random from the complete data set.
The complete likelihood model combines the prior probability with the sequencing data to derive the posterior probability of the alleles.
The probability of completing the first video layer broadcast before the deadline can be computed as,.
The probability of completing the whole process of v days is (1− β1+β2+ q )) v.
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