The Separatrix Algorithm for Synthesis and Analysis of Stochastic Simulations with Applications in Disease Modeling
Figure 2
One-dimensional hyperbolic tangent analysis.
(A) Shown are the true success probability function (dashed line), LHS samples (full and empty circles), the inferred distribution (hypercolor), and the most likely value (black line). The vertical magenta line is at the separatrix corresponding to an interest level of
. (B) The probability density after observing
samples using the Separatrix Algorithm. Note that the estimate is tight near the separatrix. (C) The inner workings of the igBDOE algorithm. First, test and sample points are loaded from the previous iteration in which they were sampled from the variance of the interest distribution, solid black (left axis), which in turn is computed from the interest distribution:
is in blue-dash and
is in red dash-dot. The expected KL divergence is plotted for each of the candidate sample points (green circles, right axis). The best
of these candidates, indicated by red crosses, will be selected. (D) The final density estimate shows that the igBDOE algorithm was placing samples in and around the separatrix. Ticks on the x-axis represent samples.