Prediction Diversity and Selective Attention in the Wisdom of Crowds

The wisdom of crowds is the idea that the combination of independent estimates of the magnitude of some quantity yields a remarkably accurate prediction, which is always more accurate than the average individual estimate. In addition, it is largely believed that the accuracy of the crowd can be improved by increasing the diversity of the estimates. Here we report the results of three experiments to probe the current understanding of the wisdom of crowds, namely, the estimates of the number of candies in a jar, the length of a paper strip and the number of pages of a book. We find that the collective estimate is better than the majority of the individual estimates in all three experiments. In disagreement with the prediction diversity theorem, we find no significant correlation between the prediction diversity and the collective error. The poor accuracy of the crowd on some experiments leads us to conjecture that its alleged accuracy is most likely an artifact of selective attention.

Evaluation of consensus strategies for haplotype phasing

Haplotype phasing is a critical step for many genetic applications but incorrect estimates of phase can negatively impact downstream analyses. One proposed strategy to improve phasing accuracy is to combine multiple independent phasing estimates to overcome the limitations of any individual estimate. However, such a strategy is yet to be thoroughly explored. This study provides a comprehensive evaluation of consensus strategies for haplotype phasing. We explore the performance of different consensus paradigms, and the effect of specific constituent tools, across several datasets with different characteristics and their impact on the downstream task of genotype imputation. Based on the outputs of existing phasing tools, we explore two different strategies to construct haplotype consensus estimators: voting across outputs from multiple phasing tools and multiple outputs of a single non-deterministic tool. We find that the consensus approach from multiple tools reduces SE by an average of 10% compared to any constituent tool when applied to European populations and has the highest accuracy regardless of population ethnicity, sample size, variant density or variant frequency. Furthermore, the consensus estimator improves the accuracy of the downstream task of genotype imputation carried out by the widely used Minimac3, pbwt and BEAGLE5 tools. Our results provide guidance on how to produce the most accurate phasing estimates and the trade-offs that a consensus approach may have. Our implementation of consensus haplotype phasing, consHap, is available freely at https://github.com/ziadbkh/consHap. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Evaluation of consensus strategies for haplotype phasing

MotivationHaplotype phasing is a critical step for many genetic applications but incorrect estimates of phase can negatively impact downstream analyses. One proposed strategy to improve phasing accuracy is to combine multiple independent phasing estimates to overcome the limitations of any individual estimate. As such a strategy is yet to be thoroughly explored, this study provides a comprehensive evaluation of consensus strategies for haplotype phasing, exploring their performance, along with their constituent tools, across a range of real and simulated datasets with different data characteristics and on the downstream task of genotype imputation.ResultsBased on the outputs of existing phasing tools, we explore two different strategies to construct haplotype consensus estimators: voting across outputs from multiple phasing tools and multiple outputs of a single non-deterministic tool. We find the consensus approach from multiple tools reduces switch error by an average of 10% compared to any constituent tool when applied to European populations and has the highest accuracy regardless of population ethnicity, sample size, SNP-density or SNP frequency. Furthermore, a consensus provides a small improvement indirectly the downstream task of genotype imputation regardless of which genotype imputation tools were used. Our results provide guidance on how to produce the most accurate phasing estimates and the tradeoffs that a consensus approach may have.AvailabilityOur implementation of consensus haplotype phasing, consHap, is available freely at https://github.com/ziadbkh/consHap.

ESTIMASI POPULASI KETAM KENARI (Birgus latro ) PULAU SIOMPU

Estimation of the Birgus latro population on the siompu island applied at the supralitora zona, that is the zona of A (north coastal supralitoral), the zona of B (east coastal supralitoral), the zona of C (south coastal supralitoral), and the zona of D (west coastal supralitoral). Objective study was presume of Birgus latro population and sex ratio on the Siompu Island. This research used capture recapture method. Analysis of the population estimation is use anticipation formula by Petersen while female and male ratio use descriptively analysis. Results of research showed that estimate value of population in each of the zona supralitoral area is unegual that is the zona of A is 7,5 or ± 8 individual, the zona of B is 10 individual, the zona of C is 14.4 or ± 14 individual, and the zona of D is 12 individual. Estimate value of the Birgus latro population size on the supralitoral zona of siompu island is 43.9 or ± 44 individual. Male and female ratio in each that; the zona of A is 4:3, the zona of B is 6:2 or 3:1, the zona of C is 9:3 or 3:1, and the zona of D is 8:1. Male and female ratio of Birgus latro at the supralitoral zona of Siompu Island is 27:9 or 3:1.

Cooperative Kalman Filtering With Data Fusion in Time Varying Communication Networks

We consider a time varying sensor network comprised of a group of agents equipped with communication capabilities, and we address applications where communication between agents is highly bandwidth limited as for instance in underwater missions. We use the Bayesian formalism to derive data fusion equations in which each sensor maintains an individual estimate of the state of a dynamical process. Data sharing between agents is defined by a time-varying network topology. We show that error covariances associated to estimates obtained with the independent opinion pool fusion scheme asymptotically agree if the communication network is partially asynchronous.