Link in intro is broken for function map turing example

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sir-julia/markdown/function_map_turing/function_map_turing.md

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In this notebook we demonstrate how to implement a discrete time function map model in Julia, and perform Bayesian inference using Turing.jl. The model is a simple SIR model with an additional state variable to track cumulative incidence. We assume that the number of observed cases in each time step is a binomial sample of the true incidence, with a reporting probability `q`. The structure of this notebook is similar to that of the [Markov POMP tutorial](https://github.com/epirecipes/sir-julia/markdown/markov_pomp/markov_pomp.md), except by choosing a function map rather than a Markov model, we can use automatic differentiation and the NUTS sampler in Turing.jl, as (a) there is no random number generation during inference and (b) all the parameters are continuous.

As the title.