Bayesian Computation with Application to Spatial Models and Neuroimaging

Analysis of Neuroimaging data has experienced great strides over the last few decades. Two key aspects of Neuroimaging data are its high-dimensionality and complex spatio-temporal autocorrelation. Classical approaches are somewhat limited in dealing with these two issues, as a result, Bayesian approaches are being utilized more frequently due to their flexibility. Despite their flexibility, there are several challenges for Bayesian approaches with respect to the required computation. First, the need for an efficient posterior computation method is paramount. Second, even in conjugate models, statistical accuracy in Bayesian computation may be hard to achieve. Since accuracy is of primary concern when studying the human brain, a careful and innovative exploration of Bayesian models and computation is necessary. In this dissertation, we address some of these issues by looking at various Bayesian computational algorithms in terms of both accuracy and speed in the context of Neuroimaging data. The algorithms we study are the Hamiltonian Monte Carlo (HMC), Variational Bayes (VB), and integrated nested Laplace approximation (INLA) algorithms. HMC is a MCMC method that's particularly powerful for sampling in high-dimensional space with highly correlated parameters. It's robust and accurate, yet not as fast as some approximate Bayesian methods, for example, Variational Bayes (VB). However, since there is no theoretical guarantee that the resulting posterior derived from VB is accurate, its performance has to be analyzed on a case-by-case basis. INLA is another extremely fast method based on numerical integration with Laplace approximations but, like VB, there are no generally applicable theoretical guarantees of accuracy. In Chapter II we focus on a particular spatial point process model, namely the log Gaussian Cox Process (LGCP), and consider applications to ecological and neuroimaging data. Inference for the LGCP is challenging due to its non-conjugacy and doubly stochastic property. We develop HMC and VB algorithms for the LGCP model and make comparisons with INLA. In Chapter III, we turn our focus to the general linear model with autoregressive errors (GLM-AR) which is widely used in analyzing fMRI single subject data. We derive an HMC algorithm and compare it with the VB algorithm and the mass univariate approach using the Statistical Parametric Mapping (SPM) software program. In Chapter IV, we extend the original GLM-AR model to a new model where the order of the AR coefficients can varying spatially across the brain and call it GLM with spatially varying autoregressive orders (SVARO). Using simulations and real data we compare our SVARO model with GLM-AR model implemented under both our MCMC sampler and the SPM VB algorithm. Our results shed light on several important issues. While HMC almost always yields the most accurate results, the performance of VB is strongly model specific. INLA is a fast alternative to MCMC methods but we observe some limitations when examining its accuracy in certain settings. Furthermore, our new SVARO model performs better than the GLM-AR model in a number of ways. Not surprisingly, more accurate algorithms generally require more computational time. By systematically evaluating the pros and cons of each method, we believe our work to be practically useful for those researchers considering the use of these methods.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138616/1/tengming_1.pd

Vehicle-to-vehicle communication: design, performance, and disruption mitigation in real-world environment

This thesis investigates the performance of 802.11p-based V2V communication in real-life scenarios, and explores potential practical applications such as GNSS correction data broadcasting to improve the positioning accuracy of nearby vehicles, and enhancing communication robustness by preemptively predicting potential disruptions with the assistance of Machine Learning (ML) models. A custom V2V On-board Unit (OBU) hardware platform was developed, and real- world multi-vehicle outdoor experiments were planned and carried out. The collected data was examined and used to train a number of ML models, and their performance was compared. The experiments revealed that the custom OBU was fully functional, and signal quality and communication range were observed to be affected by real-world imperfections. The GNSS correction data broadcasting was shown to notably increase the positioning accuracy of nearby vehicles, and the ML models trained from Key Performance Indicators (KPIs) demonstrated excellent prediction accuracy, allowing pre-emptive actions to be taken to reduce the downtime from communication disruption

Connected vehicles for internet access: deployment and spectrum policies

Internet traffic from mobile users has been growing sharply. To meet the needs of thoseusers, it is important to expand capacity of networks that provide Internet access in cost effectiveways. This capacity has traditionally been provided by cellular networks. However,expanding the capacity of those networks alone may not be the most cost-effective way to meetthe present and future growth of mobile Internet under some circumstances. In this dissertation,we show that networks of connected vehicles can be an important way to complement thecapacity of cellular networks to provide mobile Internet access under several scenarios.Connected vehicles may soon be widely deployed, forming mesh networks of short-rangeconnections among vehicles and between vehicles and roadside infrastructure. Theseconnections are collectively referred to as vehicle-to-everything, or V2X. Deployment ofconnected vehicles and infrastructure is primarily intended to enhance road safety, and the U.S.Department of Transportation has recently proposed a mandate of V2X devices in vehiclesusing Dedicated Short Range Communications (DSRC) technology. Other applications are alsoenvisioned that include Internet access in vehicles connecting to roadside infrastructure servingas gateways to the Internet.In this work, we find that V2X-based networks are more cost-effective than cellular toprovide Internet access, in scenarios which DSRC devices are mandated in vehicles to enhanceroad safety. This is true initially for densely populated urban areas, but over time V2X-basednetworks would be cost-effective in less populated areas as well, as long as Internet traffic orpenetration of V2X devices grow as expected.Local and state governments are expected to deploy roadside infrastructure for safetyapplications. If that infrastructure is shared with Internet Service Providers for a fee, then V2XABSTRACT based networks are cost-effective in locations with even lower population densities than thelocations where it is cost-effective to deploy infrastructure for Internet access only. Moreover,the sharing fee could help governments save in infrastructure costs. We find the pricingstrategies that maximize either cost-effectiveness or government savings. We estimate thatgovernments could save about one-fifth of the total cost to deploy safety infrastructurenationwide in the U.S., if fees are set to maximize government savings. Although we find thatthese prices may differ from the pricing strategy that maximizes cost-effectiveness, maximizinggovernment savings results in near-optimal cost-effectiveness.The U.S. Federal Communications Commission has allocated 75 MHz of spectrum to beused exclusively by DSRC devices, and it has been hotly debated whether all or part of thatbandwidth should be shared with unlicensed devices. We find that it is highly efficient to shareany spectrum allocated to V2X communications beyond the portion of that spectrum that isneeded for safety-critical DSRC messages. V2X and unlicensed devices require up to 50% lessbandwidth on shared spectrum to achieve given throughputs, compared to V2X and unlicenseddevices using separate bands. We conclude that the spectrum available for V2X should bemaintained or increased, as long as much of that spectrum is shared with non-V2X devices.Conclusions are derived from an engineering-economic approach, in which part of theassumptions are based on data from a citywide deployment of connected vehicles in Portugal.The data is used in a detailed and realistic packet-level simulation model of V2X-basednetworks used to provide Internet access with DSRC technology. In some scenarios, thesimulation also includes unlicensed devices using Wi-Fi technology. The results of the networksimulation are then fed into engineering-economic models to compare costs of V2X-basednetworks with costs of macrocellular networks to carry given amounts of Internet traffic, and toestimate other measures such as government revenues and spectrum usage. Those measureshelp inform decisions about where and when to deploy V2X-based networks, decisions about whether and how to promote public-private partnerships to deploy V2X infrastructure, anddecisions about sharing spectrum used for V2X communications with non-V2X devices. <br

Vehicle-to-vehicle communication: design, performance, and disruption mitigation in real-world environment

This thesis investigates the performance of 802.11p-based V2V communication in real-life scenarios, and explores potential practical applications such as GNSS correction data broadcasting to improve the positioning accuracy of nearby vehicles, and enhancing communication robustness by preemptively predicting potential disruptions with the assistance of Machine Learning (ML) models. A custom V2V On-board Unit (OBU) hardware platform was developed, and real- world multi-vehicle outdoor experiments were planned and carried out. The collected data was examined and used to train a number of ML models, and their performance was compared. The experiments revealed that the custom OBU was fully functional, and signal quality and communication range were observed to be affected by real-world imperfections. The GNSS correction data broadcasting was shown to notably increase the positioning accuracy of nearby vehicles, and the ML models trained from Key Performance Indicators (KPIs) demonstrated excellent prediction accuracy, allowing pre-emptive actions to be taken to reduce the downtime from communication disruption