• Corpus ID: 17591360

Network Inference from a Link-Traced Sample using Approximate Bayesian Computation

@article{Davis2017NetworkIF,
  title={Network Inference from a Link-Traced Sample using Approximate Bayesian Computation},
  author={Jack Davis and Steven K. Thompson},
  journal={ArXiv},
  year={2017},
  volume={abs/1701.03861}
}
We present a new inference method based on approximate Bayesian computation for estimating parameters governing an entire network based on link-traced samples of that network. To do this, we first take summary statistics from an observed link-traced network sample, such as a recruitment network of subjects in a hard-to-reach population. Then we assume prior distributions, such as multivariate uniform, for the distribution of some parameters governing the structure of the network and behaviour… 

References

SHOWING 1-10 OF 23 REFERENCES

Network inference using informative priors

This article addresses the question of incorporating prior information into network inference with focus on directed models called Bayesian networks, and introduces prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity.

Graph metrics as summary statistics for Approximate Bayesian Computation with application to network model parameter estimation

The overall findings are that Approximate Bayesian Computation can result in reasonable estimates of the parameter posteriors of graph generators relative to an observed graph, if the rank of the metrics is sufficiently high.

Approximate Bayesian computation in population genetics.

A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty.

Combining link-tracing sampling and cluster sampling to estimate the size of hidden populations

A variant of Link-Tracing Sampling which avoids the ordinary assumption of an initial Bernoulli sample of members of the target population and traces only the links between the sampled sites and the nominees.

Latent Space Approaches to Social Network Analysis

This work develops a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space,” and proposes Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates.

Non-linear regression models for Approximate Bayesian Computation

A machine-learning approach to the estimation of the posterior density by introducing two innovations that fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling.

Monte Carlo Methods of Inference for Implicit Statistical Models

Methods of inference which can be used for implicit statistical models whose distribution theory is intractable are developed, and the kernel method of probability density estimation is advocated for estimating a log-likelihood from simulations of such a model.

Dynamic spatial and network sampling

This paper considers some designs for sampling and interventions in dynamic networks and spatial temporal settings based on simple stochastic processes and investigates the effectiveness of different designs for finding units on which to make observations and introduce interventions.

Adaptive Web Sampling

A flexible class of adaptive sampling designs is introduced for sampling in network and spatial settings that have certain advantages compared with previously existing adaptive and link-tracing designs, including control over sample sizes and of the proportion of effort allocated to adaptive selections.

Statistical Analysis of Network Data with R

This book is a practical introduction to the visualization, modeling and analysis of network data, a topic which has enjoyed a recent surge in popularity and aims to “strike a balance between the two”.