Q: In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
Within the ABC-SMC approach [1], particles are first generated from the prior distribution. Particles are then resampled from the obtained sample, and slightly perturbed. From these resampled particles, a new sample is formed, from which again particles are resampled, etc. The threshold value for the summary statistic -below which new particles are accepted -is lowered with every newly obtained sample. As a result, the acceptance rate decreases and the acceptance threshold approaches zero with an increase in the number of iterations (resamplings). Initial -values were estimated as follows. We generated 100 trees and we calculated the summary statistics (indices) and used the standard deviation of this distribution as the initial values. The -values were decreased in an exponential fashion as the sequential ABC scheme progresses. Set the population indicator t = 1 Set the particle indicator i = 1 If t = 1, sample (m , ) from the prior (m, ) = (m)(|m) If t > 1 sample m with probability t1 (m ) and perturb m Km t (m|m ) Sample from the previous population {(m ) t1 } with weights w t1 . Perturb the particle, KP t,m (| ) where KP t,m is the particle perturbation kernel. If (m , ) = 0, repeat this step. Simulate a candidate dataset x f (x|m , ) If (x , y) > repeat this step. 1/3
A:
Inference of transmission network structure from HIV phylogenetic trees Supplementary information Approximate Bayesian Computation for model choice