diff --git a/algorithms/mclmc.html b/algorithms/mclmc.html index 668d425..566bc7d 100644 --- a/algorithms/mclmc.html +++ b/algorithms/mclmc.html @@ -671,8 +671,8 @@
We now consider a more complex model, of stock volatility.
The returns \(r_n\) are modeled by a Student’s-t distribution whose scale (volatility) \(R_n\) is time varying and unknown. The prior for \(\log R_n\) is a Gaussian random walk, with an exponential distribution of the random walk step-size \(\sigma\). An exponential prior is also taken for the Student’s-t degrees of freedom \(\nu\). The generative process of the data is:
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