Bayesian Claims analysis using Turing.jl

Analyze life claims experience data using Bayesian Markov-Chain-Monte-Carlo (MCMC) techniques.

The image above shows a Pluto.jl notebook which shows how to calculate posterior parameters for mortality rates.

Instructions to Run

Assuming that you already have Julia installed but still need to install Pluto notebooks:

Open a Julia REPL and copy and paste the following:

# install these dependencies
import Pkg; Pkg.add(["Pluto"]) 

# use and start Pluto
using Pluto; Pluto.run()

In the Pluto window that opens, enter this URL into the Open from file: box:

https://raw.githubusercontent.com/JuliaActuary/Learn/master/Bayesian%20Mortality%20Experience.jl

The packages in JuliaActuary are open-source and liberally licensed (MIT License) to allow wide private and commercial usage of the packages, like the base Julia language and many other packages in the ecosystem.