From 2696be48b279bb30cbe4e98ce7a863c29b5716b8 Mon Sep 17 00:00:00 2001 From: Arnau Quera-Bofarull Date: Mon, 25 Sep 2023 15:08:57 +0100 Subject: [PATCH] Update README.md --- examples/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/README.md b/examples/README.md index 9d5ff72..d586c7a 100644 --- a/examples/README.md +++ b/examples/README.md @@ -9,7 +9,7 @@ Here we include multiple examples showcasing the utility of Blackbirds to perfor The random walk process we considered is given by $$ -x(t+1) = x(t) + 2\epsilon -1, \;\;\; \epsilon \sim \mathrm{Bernoulli}(p) +x(t+1) = x(t) + 2\epsilon -1, \epsilon \sim \mathrm{Bernoulli}(p) $$ and the inference exercise consists on inferring the value of $p$ from an observed time-series $\{x(t)\}_t$. In `smd/01-random_walk.ipynb` we recover $p$ by just employing gradient descent to minimize the L2 distance between the simulated and the observed time-series. A Bayesian approach using generalized variational inference (GVI) is shown in `variational_inference/01-random_walk.ipynb`. In this case we consider the candidate family to approximate the generalised posterior as a family of normal distributions where we vary the mean $\mu$ and standard deviation $\sigma$.