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Bayesian Conditional Transformation Models
Recent developments in statistical regression methodology establish flex...
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Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Recurrent neural networks (RNNs) with rich feature vectors of past value...
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A Unifying Network Architecture for Semi-Structured Deep Distributional Learning
We propose a unifying network architecture for deep distributional learn...
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Assessment and adjustment of approximate inference algorithms using the law of total variance
A common method for assessing validity of Bayesian sampling or approxima...
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bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Over the last decades, the challenges in applied regression and in predi...
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In Search of Lost Edges: A Case Study on Reconstructing Financial Networks
To capture the systemic complexity of international financial systems, n...
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Marginally-calibrated deep distributional regression
Deep neural network (DNN) regression models are widely used in applicati...
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Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
We propose a new highly flexible and tractable Bayesian approach to unde...
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Bayesian Inference for Regression Copulas
We propose a new semi-parametric distributional regression smoother for ...
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Multivariate Conditional Transformation Models
Regression models describing the joint distribution of multivariate resp...
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Bayesian Effect Selection in Structured Additive Distributional Regression Models
We propose a novel spike and slab prior specification with scaled beta p...
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More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach
Exposure to green space seems to be beneficial for self-reported mental ...
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Implicit Copulas from Bayesian Regularized Regression Smoothers
We show how to extract the implicit copula of a response vector from a B...
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Boosting Joint Models for Longitudinal and Time-to-Event Data
Joint Models for longitudinal and time-to-event data have gained a lot o...
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