Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

Bayesian Mode Regression

This article has been made available through the Brunel Open Access Publishing Fund.Like mean, quantile and variance, mode is also an important measure of central tendency of a distribution. Many practical questions, particularly in the analysis of big data, such as \Which element (gene or le or signal) is the most typical one among all elements in a network?" are directly related to mode. Mode regression, which provides a convenient summary of how the regressors a ect the conditional mode, is totally di erent from other models based on conditional mean or conditional quantile or conditional variance. Some inference methods for mode regression exist but none of them is from the Bayesian perspective. This paper introduces Bayesian mode regression by exploring three different approaches, including their theoretic properties. The proposed approacher are illustrated using simulated datasets and a real data set

Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis

Background Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia. Methodology/Principal Findings We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR) model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects. Conclusions/Significance A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control

Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios

A new method to estimate mortality in crisis-affected and resource-poor settings: validation study

Background Data on mortality rates are crucial to guide health interventions in crisis-affected and resource-poor settings. The methods currently available to collect mortality data in such settings feature important methodological limitations. We developed and validated a new method to provide near real-time mortality estimates in such settings

Bayesian analysis of neutrinos observed from supernova SN 1987A

We present a Bayesian analysis of the energies and arrival times of the neutrinos from supernova SN 1987A detected by the Kamiokande II, IMB, and Baksan detectors, and find strong evidence for two components in the neutrino signal: a long time scale component from thermal Kelvin-Helmholtz cooling of the nascent neutron star, and a brief (~< 1 s), softer component similar to that expected from emission by accreting material in the delayed supernova scenario. In the context of this model, we show that the data constrain the electron antineutrino rest mass to be less than 5.7 eV with 95% probability. Our analysis takes advantage of significant advances that have occured in the years since the detections in both our understanding of the supernova mechanism and our ability to analyze sparse data. As a result there are substantial differences between our inferences and those found in earlier studies. We find that two-component models for the neutrino signal make the data >100 times more probable than single-component models. In addition, the radius and binding energy of the nascent neutron star implied by single-component models deviates significantly from the values predicted by current neutron star models, whereas those implied by models with an accretion component are in complete agreement with the predictions. As a result, two-component models are hundreds to thousands of times more probable than single-component models. The neutrino data thus provide the first direct observational evidence in favor of the delayed supernova scenario over the prompt scenario. (Abridged abstract)Comment: 46 pages, 12 figures, RevTeX; for submission to Physical Review

Community Risk Factors for Ocular Chlamydia Infection in Niger: Pre-Treatment Results from a Cluster-Randomized Trachoma Trial

Trachoma is one of the most important neglected tropical diseases because it is the leading cause of blindness from an infection in the world. There are about 1.3 million persons blind from the disease and many more at risk of blindness in the future. It is caused by the common bacterium Chlamydia trachomatis and can be treated with mass drug administrations (MDA) of azithromycin. We have begun a clinical trial in Niger, a country with limited resources in Africa, to determine the best treatment strategy. Our study from May to July 2010, which began before MDA's were given, showed that 26% of children aged 0–5 years were infected with the disease. In these children, we found that discharge from the nose, presence of flies on the face, and the number of years of education completed by the head of the household were risk factors for infection in 48 different communities. We hope to use this information about risk factors of infection to help guide future studies for trachoma and also to help with the WHO goal of eliminating the disease worldwide by the year 2020

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing