Search Results for author:
Found 8 papers, 4 papers with code
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests.
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing
We prove a convergence theorem for U-statistics of degree two, where the data dimension $d$ is allowed to scale with sample size $n$.
Joint Entropy Search for Multi-objective Bayesian Optimization
Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives.
Can a latent Hawkes process be used for epidemiological modelling?
We propose a Kernel Density Particle Filter (KDPF) for inference of both latent cases and reproduction number and for predicting the new cases in the near future.
BART-based inference for Poisson processes
The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non-parametric regression and classification.
Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update
Our model estimates these changes by calculating backwards from temporal data on observed to estimate the number of infections and rate of transmission that occurred several weeks prior, allowing for a probabilistic time lag between infection and death.
Applications Methodology
Quantifying demand and weather uncertainty in power system models using the m out of n bootstrap
The methodology introduced in this paper quantifies demand & weather uncertainty using a time series bootstrap scheme with shorter time series than the original, enhancing computational efficiency and avoiding the need for any additional data.
Applications
Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure.
