We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country $i$ took action $a$ toward country $j$ at time $t$." BPTD discovers overlapp...
Cross-series dependencies are crucial in obtaining accurate forecasts when forecasting a multivariate time series. Simultaneous Graphical Dynamic Linear Models (SGDLMs) are Bayesian models that elegantly capture cross-series dependencies. This study...
The paper is proposing a survey of a region in Sudan, the Bayuda desert, using the satellite images as obtained from Google Maps. The images reveal the ring granitic structure of the region enclosed by a bend of river Nile. To enhance the features of...
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1 day ago · Treat those you love to casual, yet elegant waterfront dining on the Great South Bay at Captain Bill's, where you can savor the spectacular sunset over the water while enjoying a fresh daily …
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We hope Bay View will provide a quantum leap in the evolution of the workplace, elevate the benchmark for sustainable design, and inspire the next generations of users and visitors to the building." Leon …
Bayesian hierarchical models are a methodology for aggregation and synthesis of data from heterogeneous settings, used widely in statistics and other disciplines. I apply this framework to the evidence from 7 randomized experiments of expanding acces...
We implement a test of the variability of the per-cycle annual modulation amplitude in the different phases of the DAMA/LIBRA experiment using Bayesian model comparison. Using frequentist methods, a previous study (Kelso et al 2018) had demonstrated...
Variable selection over a potentially large set of covariates in a linear model is quite popular. In the Bayesian context, common prior choices can lead to a posterior expectation of the regression coefficients that is a sparse (or nearly sparse) vec...
A common test for the diagnosis of type 2 diabetes is the Oral Glucose Tolerance Test (OGTT). Recent developments in the study of OGTT tests have framed it as a Bayesian inverse problem. These data analysis advances promise great improvements in the...
This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic blockmodel (SBM), as well as its degree-corrected and overlapping generalization...
We hope Bay View will provide a quantum leap in the evolution of the workplace, elevate the benchmark for sustainable design, and inspire the next generations of users and visitors to the building." Leon …
Branching Time Active Inference (Champion et al., 2021b,a) is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in Active Inference (Friston et al., 2016; Da Costa et al., 2020; Champion et al., 20...
A demanding challenge in Bayesian inversion is to efficiently characterize the posterior distribution. This task is problematic especially in high-dimensional non-Gaussian problems, where the structure of the posterior can be very chaotic and difficu...