Titlebook: Statistical Foundations of Actuarial Learning and its Applications; Mario V. Wüthrich,Michael Merz Book‘‘‘‘‘‘‘‘ 2023 The Authors 2023 Open

預(yù)測(cè)

echnic

Convolutional Neural Networks,deling, e.g., applied to image recognition problems. Time-series and images have a natural topology, and CN networks try to benefit from this additional structure (over tabular data). We introduce these network architectures and provide insurance-relevant examples related to telematics data and mort

CHASE

Natural Language Processing, words can be embedded into low-dimension spaces that serve as numerical word encodings. These can then be used for text recognition, either using RN networks or attention layers. We give an example where we aim at predicting claim perils from claim descriptions.

hermitage

滲入

Osteons

Predictive Modeling and Forecast Evaluation,odels. This chapter is complemented by a more decision-theoretic approach to forecast evaluation, it discusses deviance losses, proper scoring, elicitability, forecast dominance, cross-validation, Akaike’s information criterion (AIC) and we give an introduction to the bootstrap simulation method.

卡死偷電

Deep Learning,e model uncertainty. This chapter is complemented by many examples on non-life insurance pricing, but also on mortality modeling, as well as tools that help to explain deep FN network regression results.

Integrate

鐵砧

拉開(kāi)這車床

Statistical Foundations of Actuarial Learning and its Applications978-3-031-12409-9Series ISSN 2523-3262 Series E-ISSN 2523-3270