-
A Novel Method of Marginalisation using Low Discrepancy Sequences for Integrated ...
Recently, it has been shown that approximations to marginal posterior distributions obtained using a low discrepancy sequence (LDS) can outperform standard grid-based methods with respect to both accuracy and computational effic ... Read More >
-
Enabling Personalized Decision Support with Patient-Generated Data and Attributa ...
Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information i ... Read More >
-
Adversarial Risk Analysis for First-Price Sealed-Bid Auctions
Adversarial Risk Analysis (ARA) is an upcoming methodology that is considered to have advantages over the traditional decision theoretic and game theoretic approaches. ARA solutions for first-price sealed-bid (FPSB) auctions hav ... Read More >
-
Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelia ...
Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challenge in MR is that each genetic variant explains a ... Read More >
-
Random Machines: A bagged-weighted support vector model with free kernel choice
Improvement of statistical learning models in order to increase efficiency in solving classification or regression problems is still a goal pursued by the scientific community. In this way, the support vector machine model is on ... Read More >
-
Detection of Two-Way Outliers in Multivariate Data and Application to Cheating D ...
The paper proposes a new latent variable model for the simultaneous (two-way) detection of outlying individuals and items for item-response-type data. The proposed model is a synergy between a factor model for binary responses a ... Read More >
-
Bayesian optimization with local search
Global optimization finds applications in a wide range of real world problems. The multi-start methods are a popular class of global optimization techniques, which are based on the ideas of conducting local searches at multiple ... Read More >
-
Replication-based emulation of the response distribution of stochastic simulator ...
Due to limited computational power, performing uncertainty quantification analyses with complex computational models can be a challenging task. This is exacerbated in the context of stochastic simulators, the response of which t ... Read More >
-
Mixtures of multivariate generalized linear models with overlapping clusters
With the advent of ubiquitous monitoring and measurement protocols, studies have started to focus more and more on complex, multivariate and heterogeneous datasets. In such studies, multivariate response variables are drawn from ... Read More >
-
Assessment and adjustment of approximate inference algorithms using the law of t ...
A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under continuity assumptions, quantiles of functions of the ... Read More >