ZüKoSt: Seminar on Applied Statistics

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Autumn Semester 2021

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29 October 2021
15:15-16:15

Sebastian Sippl
ETH Zurich

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ZüKoSt Zürcher Kolloquium über Statistik

Title	Characterizing forced climate signals and internal variability in observations and models
Speaker, Affiliation	Sebastian Sippl, ETH Zurich
Date, Time	29 October 2021, 15:15-16:15
Location	HG G 19.1
Abstract	Joint work with: Nicolai Meinshausen, Erich Fischer, Eniko Székely, Flavio Lehner, Angeline Pendergrass, Reto Knutti Internal climate variability fundamentally limits short- and medium-term climate predictability, and the separation of forced changes from internal variability is a key goal in climate change detection and attribution (D&A). In this talk, we discuss the identification of forced climate signals and internal variability in observations and models from spatial patterns of climate variables by using statistical learning techniques. We first introduce a detection approach using climate model simulations and a statistical learning algorithm to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature. Observations are then projected onto this relationship to detect climatic changes, and it is shown that externally forced climate change can be assessed and detected in the observed global climate record at time steps such as months or days. Second, we discuss how these approaches can be extended to address key remaining uncertainties related to the role of decadal-scale internal variability (DIV). DIV is difficult to quantify accurately from observations, and D&A requires that models simulate internal climate variability sufficiently accurately. We show that a recently developed statistical learning technique, anchor regression, allows to identify the externally forced global temperature response, while increasing the robustness towards different representations of DIV (via an explicit `anchor’ on decadal-scale variability). The fraction of warming due to external factors, based on these optimized patterns, is more robust across different climate models even if DIV would be larger than current best estimates. These findings increase the confidence that warming over past decades is dominated by external forcing, irrespective of remaining uncertainties in the magnitude of climate variability

Characterizing forced climate signals and internal variability in observations and models read_more

HG G 19.1

12 November 2021
15:15-16:15

Leonhard Held
Universität Zürich

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ZüKoSt Zürcher Kolloquium über Statistik

Title	The Statistical Assessment of Replication Success - A Case for Reverse-Bayes
Speaker, Affiliation	Leonhard Held, Universität Zürich
Date, Time	12 November 2021, 15:15-16:15
Location	HG G 19.1
Abstract	Replicability of research findings is crucial to the credibility of all empirical domains of science. Large-scale replication projects are increasingly conducted in order to assess to what extent claims of new discoveries can be confirmed in independent replication studies. However, there is no established standard how to assess replication success and in practice many different approaches are used. We argue that Reverse-Bayes methods have a key role to play in the assessment of replication success. The main idea is to reverse Bayes' Theorem to determine a sceptical prior that would make the original finding no longer convincing. Sufficient incompatability of the sceptical prior and the replication study result is then used to quantify the degree of replication success. We show how this approach is directly related to the relative effect size, the ratio of the replication to the original effect estimate. This perspective leads to a new proposal to recalibrate the assessment of replication success.

The Statistical Assessment of Replication Success - A Case for Reverse-Bayesread_more

HG G 19.1

10 December 2021
15:15-16:15

Bjoern Menze
Universität Zürich

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ZüKoSt Zürcher Kolloquium über Statistik

Title	Biomedical Image Analysis and Machine Learning
Speaker, Affiliation	Bjoern Menze, Universität Zürich
Date, Time	10 December 2021, 15:15-16:15
Location
Abstract	Biomedical image data offers quantitative information about health, disease, and disease progression under treatment - both at the patient and at the population level. Computational routines are instrumental in extracting these information in a structured fashion, typically following a succession of image segmenation, 'radiomic' feature extraction, and predictive modeling with respect to a given image marker or disease-related outcome. This pipeline can also be complemented by a functional and patient-specific modeling of the features or processses underlying the given image observations, for example, the tumor-growth underlying a set of magnetic resonance scans acquired prior to and after treatment. I will talk about this image processing pipeline, together open problems that we continue to work in Zurich, focusing on two aspects: a) the development and benchmarking of image segmenation routines in the 'Multi-modal Brain Tumor Image Segmentation Benchmark' (BRATS), one of the largest benchmark challenges in biomedical image computing, and b) the image-based modeling of tumor growth using partial differential equations, and a fast personalization and inversion of those models via neural networks.

Biomedical Image Analysis and Machine Learningread_more (CANCELLED)

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