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mtkier94/README.md

Hi! 👋

This repo contains code for my PhD (2018-2022) research in machine & deep learning in the context of actuarial science. Code enables interested researches to train, test and run the respective models. Relevant papers include

1/ Kiermayer, M., Weiß, C. (2024). Neural calibration of hidden inhomogeneous Markov chains: informa- tion decompression in life insurance. Mach Learn 113, 7129–7156 (2024). https://doi.org/10.1007/s10994-024-06551-w
2/ Kiermayer, M. (2022). Modeling Surrender Risk in Life Insurance: theoretical and experimental insight. Scandinavian Actuarial Journal, 2022(7), 627–658. https://doi.org/10.1080/03461238.2021.2013308
3/ Kiermayer, M., Weiß, C. (2021). Grouping of contracts in insurance using neural networks. Scandinavian Actuarial Journal 2021(4), 295–322. https://doi.org/10.1080/03461238.2020.1836676

More generally about me:

Languages in my toolbox

  • python
  • matlab
  • R

Things that interest me include

  • understanding new modelling approaches
  • transfering ideas to new fields of application
  • speeding up code and training of models

I'm curious to learn more about

  • distributed training
  • cloud computing
  • learning theory

If you want to connect, I'll be happy to read your message on LinkedIn LinkedIn

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  2. Surrender_Analysis Surrender_Analysis Public

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  3. Neural-calibration-of-hidden-inhomogeneous-Marko-chains Neural-calibration-of-hidden-inhomogeneous-Marko-chains Public

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  4. Grouping-in-insurance-with-neural-networks Grouping-in-insurance-with-neural-networks Public

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  5. Particle_Filter Particle_Filter Public

    Code for research paper 'Estimation of the CIR Model using a Particle Filter Method' (2018), submitted at University of Waterloo.

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    Config files for my GitHub profile.