Nonparametric Bayes Modeling of Populations of Networks

Replicated network data are increasingly available in many research fields. In connectomic applications, inter-connections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for inference on the entire probability mass function of a network-valued random variable and therefore lack flexibility in characterizing the distribution of relevant topological structures. We propose a flexible Bayesian nonparametric approach for modeling the population distribution of network-valued data. The joint distribution of the edges is defined via a mixture model which reduces dimensionality and efficiently incorporates network information within each mixture component by leveraging latent space representations. The formulation leads to an efficient Gibbs sampler and provides simple and coherent strategies for inference and goodness-of-fit assessments. We provide theoretical results on the flexibility of our model and illustrate improved performance --- compared to state-of-the-art models --- in simulations and application to human brain networks

On the road to prosperity? The economic geography of China's national expressway network

Over the past two decades, China has embarked on an ambitious program of expressway network expansion. By facilitating market integration, this program aims both to promote efficiency at the national level and to contribute to the catch-up of lagging inland regions with prosperous Eastern ones. This paper evaluates the aggregate and spatial economic impacts of China's newly constructed National Expressway Network, focussing, in particular, on its short-run impacts. To achieve this aim, the authors adopt a counterfactual approach based on the estimation and simulation of a structural "new economic geography" model. Overall, they find that aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007 had the expressway network not been built. Although there is considerable heterogeneity in the results, the authors do not find evidence of a significant reduction in disparities across prefectural level regions or of a reduction in urban-rural disparities. If anything, the expressway network appears to have reinforced existing patterns of spatial inequality, although, over time, these will likely be reduced by enhanced migration

BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before

Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g. wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.Comment: 24 pages, 4 figures, 6 table

Automated analysis of eclipsing binary lightcurves. I. EBAS -- a new Eclipsing Binary Automated Solver with EBOP

We present a new algorithm -- Eclipsing Binary Automated Solver (EBAS), to analyse lightcurves of eclipsing binaries. The algorithm is designed to analyse large numbers of lightcurves, and is therefore based on the relatively fast EBOP code. To facilitate the search for the best solution, EBAS uses two parameter transformations. Instead of the radii of the two stellar components, EBAS uses the sum of radii and their ratio, while the inclination is transformed into the impact parameter. To replace human visual assessment, we introduce a new 'alarm' goodness-of-fit statistic that takes into account correlation between neighbouring residuals. We perform extensive tests and simulations that show that our algorithm converges well, finds a good set of parameters and provides reasonable error estimation.Comment: 25 pages, 13 figures, accepted to MNRA

On the road to prosperity ? The economic geography of China's national expressway network

Over the past two decades, China has embarked on an ambitious program of expressway network expansion. By facilitating market integration, this program aims both to promote efficiency at the national level and to contribute to the catch-up of lagging inland regions with prosperous Eastern ones. This paper evaluates the aggregate and spatial economic impacts of China's newly constructed National Expressway Network, focussing, in particular, on its short-run impacts. To achieve this aim, the authors adopt a counterfactual approach based on the estimation and simulation of a structural"new economic geography"model. Overall, they find that aggregate Chinese real income was approximately 6 percent higher than it would have been in 2007 had the expressway network not been built. Although there is considerable heterogeneity in the results, the authors do not find evidence of a significant reduction in disparities across prefectural level regions or of a reduction in urban-rural disparities. If anything, the expressway network appears to have reinforced existing patterns of spatial inequality, although, over time, these will likely be reduced by enhanced migration.Transport Economics Policy&Planning,Economic Theory&Research,Labor Policies,Roads&Highways,Regional Economic Development