Introduction It’s been three years since the Casualty Actuarial Society published our research paper on Hierarchical Compartmental Reserving Models (Gesmann and Morris (2020)). Time to revisit it, as developments of the Stan language, and its interfaces such as cmdstanr and brms have progressed and simplified the treatment of differential equations.
We have updated the bookdown version version of the paper to take advantage of these newer versions.
This post will give another example of how to use hierarchical compartmental reserving models, but rather than working with historical claims data, we use the model to generate future data, as may be required for a business plan of a new product, where no historical data exists.

Over the last year I worked with Jake Morris on a research paper for the Casualty Actuarial Society. We are delighted to see it published:
Gesmann, M., and Morris, J. “Hierarchical Compartmental Reserving Models.” Casualty Actuarial Society, CAS Research Papers, 19 Aug. 2020, https://www.casact.org/sites/default/files/2021-02/compartmental-reserving-models-gesmannmorris0820.pdf
The paper demonstrates how one can describe the dynamics of claims processes with differential equations and probability distributions. All of this is set into a Bayesian framework that allows us to combine judgement and historical data into a consistent framework.

How do you build a model from first principles? Here is a step by step guide.
Following on from last week’s post on Principled Bayesian Workflow I want to reflect on how to motivate a model.
The purpose of most models is to understand change, and yet, considering what doesn’t change and should be kept constant can be equally important.
I will go through a couple of models in this post to illustrate this idea.

Last Tuesday we got together for the 4th Bayesian Mixer Meetup. Product Madness kindly hosted us at their offices in Euston Square. About 50 Bayesians came along; the biggest turn up thus far, including developers of PyMC3 (Peadar Coyle) and Stan (Michael Betancourt).
The agenda had two feature talks by Dominic Steinitz and Volodymyr Kazantsev and a lightning talk by Jon Sedar.
Dominic Steinitz: Hamiltonian and Sequential MC samplers to model ecosystems Dominic shared with us his experience of using Hamiltonian and Sequential Monte Carlo samplers to model ecosystems.

The forthcoming R Journal has an interesting article about phaseR: An R Package for Phase Plane Analysis of Autonomous ODE Systems by Michael J. Grayling. The package has some nice functions to analysis one and two dimensional dynamical systems. As an example I use here the FitzHugh-Nagumo system introduced earlier: \[ \begin{aligned} \dot{v}=&2 (w + v - \frac{1}{3}v^3) + I_0 \\\\\\ \dot{w}=&\frac{1}{2}(1 - v - w)\\\\\\ \end{aligned} \] The FitzHugh-Nagumo system is a simplification of the Hodgkin-Huxley model of spike generation in squid giant axon.

I discussed earlier how the action potential of a neuron can be modelled via the Hodgkin-Huxely equations. Here I will present a simple model that describes how action potentials can be generated and propagated across neurons. The tricky bit here is that I use delay differential equations (DDE) to take into account the propagation time of the signal across the network.
My model is based on the paper: Epileptiform activity in a neocortical network: a mathematical model by F.

One of the great research papers of the 20th century celebrates its 60th anniversary in a few weeks time: A quantitative description of membrane current and its application to conduction and excitation in nerve by Alan Hodgkin and Andrew Huxley. Only shortly after Andrew Huxley died, 30th May 2012, aged 94.
In 1952 Hodgkin and Huxley published a series of papers, describing the basic processes underlying the nervous mechanisms of control and the communication between nerve cells, for which they received the Nobel prize in physiology and medicine, together with John Eccles in 1963.

This evening I will talk about Dynamical systems in R with simecol at the LondonR meeting.
Thanks to the work by Thomas Petzoldt, Karsten Rinke, Karline Soetaert and R. Woodrow Setzer it is really straight forward to model and analyse dynamical systems in R with their deSolve and simecol packages.
I will give a brief overview of the functionality using a predator-prey model as an example.
This is of course a repeat of my presentation given at the Köln R user group meeting in March.

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