About David Bolin David Bolin Professor, Statistics mathematical statistics random fields stochastic partial differential equations computational statistics stochastic processes David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics. Events Presented Events Sep 7 - Sep 13, 2025 Gaussian Random Fields on Metric Graphs David Bolin, Professor, Statistics Sep 11, 12:00 - 13:00 B9 B2 L2325 Gaussian random fields Metric graphs Statistical Modeling This talk presents a comprehensive mathematical and statistical theory, along with user-friendly software, for modeling data with Gaussian random fields on metric graphs by developing valid covariance functions based on network distance. Mar 23 - Mar 29, 2025 Statistical Models and Methods Based on Stochastic Partial Differential Equations David Bolin, Professor, Statistics Mar 23, 14:00 - 15:30 B9 L2 R2322; Zoom Meeting ID 93209452060 SPDEs non-Gaussian random fields statistical analysis Metric graphs This talk presents an overview of our research on statistical methods using stochastic partial differential equations (SPDEs), focusing on non-Gaussian random fields and fractional-order SPDEs, and theory for random fields and statistical analysis on metric graphs, highlighting theoretical contributions, software development, and applications relevant to KAUST RDI pillars. Oct 2 - Oct 8, 2022 A new class of random field models for data on networks David Bolin, Professor, Statistics Oct 3, 12:00 - 13:00 B9 L2 R2322 H1 applied statistics machine learning Gaussian processes Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs. Feb 7 - Feb 13, 2021 Proper scoring rules and model selection for stochastic processes David Bolin, Professor, Statistics Feb 11, 12:00 - 13:00 KAUST In this talk, we begin by a brief introduction to proper scoring rules and their use in statistics. Then, we discuss an often overlooked problem: the up-weighting of observations with large uncertainty, which can lead to unintuitive rankings of models, by some of the most popular proper scoring rules, such as the continuously ranked probability score (CRPS), the MAE, and the MSE.
Gaussian Random Fields on Metric Graphs David Bolin, Professor, Statistics Sep 11, 12:00 - 13:00 B9 B2 L2325 Gaussian random fields Metric graphs Statistical Modeling This talk presents a comprehensive mathematical and statistical theory, along with user-friendly software, for modeling data with Gaussian random fields on metric graphs by developing valid covariance functions based on network distance.
Statistical Models and Methods Based on Stochastic Partial Differential Equations David Bolin, Professor, Statistics Mar 23, 14:00 - 15:30 B9 L2 R2322; Zoom Meeting ID 93209452060 SPDEs non-Gaussian random fields statistical analysis Metric graphs This talk presents an overview of our research on statistical methods using stochastic partial differential equations (SPDEs), focusing on non-Gaussian random fields and fractional-order SPDEs, and theory for random fields and statistical analysis on metric graphs, highlighting theoretical contributions, software development, and applications relevant to KAUST RDI pillars.
A new class of random field models for data on networks David Bolin, Professor, Statistics Oct 3, 12:00 - 13:00 B9 L2 R2322 H1 applied statistics machine learning Gaussian processes Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.
Proper scoring rules and model selection for stochastic processes David Bolin, Professor, Statistics Feb 11, 12:00 - 13:00 KAUST In this talk, we begin by a brief introduction to proper scoring rules and their use in statistics. Then, we discuss an often overlooked problem: the up-weighting of observations with large uncertainty, which can lead to unintuitive rankings of models, by some of the most popular proper scoring rules, such as the continuously ranked probability score (CRPS), the MAE, and the MSE.
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