8,659 research outputs found
Nonparametric Bayes dynamic modeling of relational data
Symmetric binary matrices representing relations among entities are commonly
collected in many areas. Our focus is on dynamically evolving binary relational
matrices, with interest being in inference on the relationship structure and
prediction. We propose a nonparametric Bayesian dynamic model, which reduces
dimensionality in characterizing the binary matrix through a lower-dimensional
latent space representation, with the latent coordinates evolving in continuous
time via Gaussian processes. By using a logistic mapping function from the
probability matrix space to the latent relational space, we obtain a flexible
and computational tractable formulation. Employing P\`olya-Gamma data
augmentation, an efficient Gibbs sampler is developed for posterior
computation, with the dimension of the latent space automatically inferred. We
provide some theoretical results on flexibility of the model, and illustrate
performance via simulation experiments. We also consider an application to
co-movements in world financial markets
Locally Adaptive Dynamic Networks
Our focus is on realistically modeling and forecasting dynamic networks of
face-to-face contacts among individuals. Important aspects of such data that
lead to problems with current methods include the tendency of the contacts to
move between periods of slow and rapid changes, and the dynamic heterogeneity
in the actors' connectivity behaviors. Motivated by this application, we
develop a novel method for Locally Adaptive DYnamic (LADY) network inference.
The proposed model relies on a dynamic latent space representation in which
each actor's position evolves in time via stochastic differential equations.
Using a state space representation for these stochastic processes and
P\'olya-gamma data augmentation, we develop an efficient MCMC algorithm for
posterior inference along with tractable procedures for online updating and
forecasting of future networks. We evaluate performance in simulation studies,
and consider an application to face-to-face contacts among individuals in a
primary school
Bayesian dynamic financial networks with time-varying predictors
We propose a Bayesian nonparametric model including time-varying predictors
in dynamic network inference. The model is applied to infer the dependence
structure among financial markets during the global financial crisis,
estimating effects of verbal and material cooperation efforts. We interestingly
learn contagion effects, with increasing influence of verbal relations during
the financial crisis and opposite results during the United States housing
bubble
Rotor-Obstacle Aerodynamic Interaction in Hovering Flight: An Experimental Survey
Despite the presence of a fair number of numerical and experimental works on the rotor-obstacle interaction, a systematic
study of the aerodynamic phenomena involved is lacking. In this paper a comprehensive experimental survey
carried out at University of Glasgow is described, taking advantage of two different rotor rigs and several experimental
techniques. Load measurements on the rotor were carried out in order to assess the rotor performance for different
positions with respect to a cubic obstacle, thus simulating a set of possible hovering flight conditions around the obstacle.
Laser Doppler Anemometry (LDA) measurements of the rotor inflow were used in order to investigate how the
aerodynamic interaction affected the rotor performance. Eventually Stereoscopic Particle Image Velocimetry (SPIV)
measurements in the region between the rotor and the obstacle were carried out in order to have a better insight of the
interacting flow field
Convex mixture regression for quantitative risk assessment
There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health outcome. A main focus in these studies is inference on dose levels associated with a given increase in risk relative to a baseline. In addressing this goal, popular methods either dichotomize the continuous response or focus on modeling changes with the dose in the expectation of the outcome. Such choices may lead to information loss and provide inaccurate inference on dose-response relationships. We instead propose a Bayesian convex mixture regression model that allows the entire distribution of the health outcome to be unknown and changing with the dose. To balance flexibility and parsimony, we rely on a mixture model for the density at the extreme doses, and express the conditional density at each intermediate dose via a convex combination of these extremal densities. This representation generalizes classical dose-response models for quantitative outcomes, and provides a more parsimonious, but still powerful, formulation compared to nonparametric methods, thereby improving interpretability and efficiency in inference on risk functions. A Markov chain Monte Carlo algorithm for posterior inference is developed, and the benefits of our methods are outlined in simulations, along with a study on the impact of dde exposure on gestational age
Doubly Robust Smoothing of Dynamical Processes via Outlier Sparsity Constraints
Coping with outliers contaminating dynamical processes is of major importance
in various applications because mismatches from nominal models are not uncommon
in practice. In this context, the present paper develops novel fixed-lag and
fixed-interval smoothing algorithms that are robust to outliers simultaneously
present in the measurements {\it and} in the state dynamics. Outliers are
handled through auxiliary unknown variables that are jointly estimated along
with the state based on the least-squares criterion that is regularized with
the -norm of the outliers in order to effect sparsity control. The
resultant iterative estimators rely on coordinate descent and the alternating
direction method of multipliers, are expressed in closed form per iteration,
and are provably convergent. Additional attractive features of the novel doubly
robust smoother include: i) ability to handle both types of outliers; ii)
universality to unknown nominal noise and outlier distributions; iii)
flexibility to encompass maximum a posteriori optimal estimators with reliable
performance under nominal conditions; and iv) improved performance relative to
competing alternatives at comparable complexity, as corroborated via simulated
tests.Comment: Submitted to IEEE Trans. on Signal Processin
Locally adaptive factor processes for multivariate time series
In modeling multivariate time series, it is important to allow time-varying
smoothness in the mean and covariance process. In particular, there may be
certain time intervals exhibiting rapid changes and others in which changes are
slow. If such time-varying smoothness is not accounted for, one can obtain
misleading inferences and predictions, with over-smoothing across erratic time
intervals and under-smoothing across times exhibiting slow variation. This can
lead to mis-calibration of predictive intervals, which can be substantially too
narrow or wide depending on the time. We propose a locally adaptive factor
process for characterizing multivariate mean-covariance changes in continuous
time, allowing locally varying smoothness in both the mean and covariance
matrix. This process is constructed utilizing latent dictionary functions
evolving in time through nested Gaussian processes and linearly related to the
observed data with a sparse mapping. Using a differential equation
representation, we bypass usual computational bottlenecks in obtaining MCMC and
online algorithms for approximate Bayesian inference. The performance is
assessed in simulations and illustrated in a financial application
Investigation of the rotor–obstacle aerodynamic interaction in hovering flight
In this paper, a comprehensive experimental survey of the aerodynamic interaction of a hovering rotor in the proximity of a ground obstacle is described, taking advantage of multiple experimental techniques. Load measurements on the rotor were carried out to assess the change in the rotor performance for different positions with respect to the cubic obstacle, thus simulating a set of possible hovering flight conditions around the obstacle. Laser Doppler anemometry measurements of the rotor inflow were used to investigate how the aerodynamic interaction affected the rotor performance. Stereoscopic particle image velocimetry measurements in the region between the rotor and the obstacle were carried out to gain a better insight of the interacting flow field. The investigation showed two main regions of interest. The first region is the one above the edge of the obstacle, where the rotor experiences a gradual ground effect as it is positioned over the obstacle. The second region, probably of more interest, is the one just beside the obstacle where a recirculation region between the rotor and the obstacle develops, causing both a significant reduction in the thrust augmentation experienced in unobstructed hover in ground effect and significant pitching and rolling moments, due to the nonsymmetrical inflow pattern on the rotor
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
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