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Furthermore, the Girsanov transformation gives the likelihood as a function of a and b.
Further, we know the likelihood, as a function of congestion duration, that congested arcs will become uncongested and thus less costly to traverse.
In MLE, the parameters are assumed to be the unknown but deterministic constants and the measurements are used to calculate the likelihood as a function of the parameters.
However, it can effectively be attacked by alternating between optimizing the marginal likelihood as a function of p0 and the set of pattern locations, {x i,i = 1…M}, respectively.
Bottom Row: Likelihood as a function of model parameter value.
We see that the likelihood as a function of t2 increases to an asymptotic level.
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The evolution of the log-likelihood as a function of time was used to estimate if the 2 parallel chains had reached the stationary-state.
We monitored the development of the log-likelihood as a function of time and found it to be stable (to show convergence) after approximately 3000 4000 cycles.
Results were tested for convergence by plotting the change in Log-likelihood as a function of the number of iterations (Jones and Wang 2010), and only inferred sib-ships with a probability over 0.9 were plotted.
The end result of this process was to have a set of 4 log-likelihoods as a function of time (male [M, blue], female [F, red], overlap [O, green], and noise [N, black]).
Here we determine estimates for both parameter vectors simultaneously by finding the maximum of the unconditional likelihood function as a function of θ and β.
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