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Changing the prior for negative binomial observations #123

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merged 7 commits into from
Mar 8, 2024
Merged

Changing the prior for negative binomial observations #123

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d034601
Change prior for negative binomial observations
SamuelBrand1 Mar 7, 2024
a048da5
Fix test to reflect that the `r` parameter is now the square of the c…
SamuelBrand1 Mar 7, 2024
e669ff8
More information on choice of variance prior
SamuelBrand1 Mar 8, 2024
f61edb1
Merge branch 'main' into 29-sampling-noise-prior
SamuelBrand1 Mar 8, 2024
ee33080
Removed unnecessary `nchains` arg
SamuelBrand1 Mar 8, 2024
0b42618
Combined new prior into getting started
SamuelBrand1 Mar 8, 2024
cd91ae2
fix broken manypathfinder test
SamuelBrand1 Mar 8, 2024
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13 changes: 5 additions & 8 deletions EpiAware/docs/src/examples/getting_started.jl
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
### A Pluto.jl notebook ###
# v0.19.39
# v0.19.40

using Markdown
using InteractiveUtils
Expand Down Expand Up @@ -149,7 +149,7 @@ as the action of a sparse kernel on the infections $I(t)$.
```math
\begin{align}
y_t &\sim \text{NegBinomial}(\mu = \sum_{s\geq 0} K[t, t-s] I(s), r), \\
1 / r &\sim \text{Gamma}(3, 0.05/3).
1 / \sqrt{r} &\sim \text{HalfNormal}\Big(0.1 \sqrt{{\pi \over 2}}\Big).
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\end{align}
```
"
Expand All @@ -171,7 +171,7 @@ We choose a simple observation model where infections are observed 0, 1, 2, 3 da
obs_model = EpiAware.DelayObservations(
fill(0.25, 4),
time_horizon,
truncated(Gamma(5, 0.05 / 5), 1e-3, 1.0)
truncated(Normal(0, 0.1 * sqrt(pi) / sqrt(2)), 0.0, Inf)
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)

# ╔═╡ e49713e8-4840-4083-8e3f-fc52d791be7b
Expand Down Expand Up @@ -314,8 +314,6 @@ let
size = (700, 400))
end

# ╔═╡ 2293b711-0bd0-44d5-8a30-94e56c5e4c65

# ╔═╡ fd6321b1-4c3a-4123-b0dc-c45b951e0b80
md"
As well as checking the posterior predictions for latent infections, we can also check how well inference recovered unknown parameters, such as the random walk variance or the cluster factor of the negative binomial observations.
Expand Down Expand Up @@ -386,7 +384,7 @@ end
# ╠═6639e66f-7725-4976-81b2-6472419d1a62
# ╟─df5e59f8-3185-4bed-9cca-7c266df17cec
# ╠═6fbdd8e6-2323-4352-9185-1f31a9cf9012
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# ╟─0aa3fcbd-0831-45b8-9a2c-7ffbabf5895f
Expand All @@ -401,7 +399,7 @@ end
# ╠═d073e63b-62da-4743-ace0-78ef7806bc0b
# ╠═a04f3c1b-7e11-4800-9c2a-9fc0021de6e7
# ╟─f68b4e41-ac5c-42cd-a8c2-8761d66f7543
# ╠═b5bc8f05-b538-4abf-aa84-450bf2dff3d9
# ╟─b5bc8f05-b538-4abf-aa84-450bf2dff3d9
# ╠═c8ce0d46-a160-4c40-a055-69b3d10d1770
# ╟─4a4c6e91-8d8f-4bbf-bb7e-a36dc281e312
# ╠═259a7042-e74f-43c7-aeb4-97a3beeb7776
Expand All @@ -410,7 +408,6 @@ end
# ╠═3eb5ec5e-aae7-478e-84fb-80f2e9f85b4c
# ╟─30498cc7-16a5-441a-b8cd-c19b220c60c1
# ╠═e9df22b8-8e4d-4ab7-91ea-c01f2239b3e5
# ╠═2293b711-0bd0-44d5-8a30-94e56c5e4c65
# ╟─fd6321b1-4c3a-4123-b0dc-c45b951e0b80
# ╠═10d8fe24-83a6-47ac-97b7-a374481473d3
# ╟─81efe8ca-b753-4a12-bafc-a887a999377b
Expand Down
5 changes: 3 additions & 2 deletions EpiAware/src/observation-models.jl
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Expand Up @@ -26,7 +26,8 @@ struct DelayObservations{T <: AbstractFloat, S <: Sampleable} <: AbstractObserva
end

function default_delay_obs_priors()
return (:neg_bin_cluster_factor_prior => Gamma(3, 0.05 / 3),) |> Dict
return (:neg_bin_cluster_factor_prior => truncated(
Normal(0, 0.1 * sqrt(pi) / sqrt(2)), 0.0, Inf),) |> Dict
end

function generate_observations(observation_model::AbstractObservationModel,
Expand Down Expand Up @@ -54,7 +55,7 @@ end

for i in eachindex(y_t)
y_t[i] ~ NegativeBinomialMeanClust(
expected_obs[i], neg_bin_cluster_factor
expected_obs[i], neg_bin_cluster_factor^2
)
end

Expand Down
2 changes: 1 addition & 1 deletion EpiAware/test/test_observation-models.jl
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Expand Up @@ -25,7 +25,7 @@
chn -> generated_quantities(fix_mdl, chn) .|>
(gen -> gen[1][1]) |>
vec
direct_samples = EpiAware.NegativeBinomialMeanClust(I_t[1], neg_bin_cf) |>
direct_samples = EpiAware.NegativeBinomialMeanClust(I_t[1], neg_bin_cf^2) |>
dist -> rand(dist, n_samples)

#For discrete distributions, checking mean and variance is as expected
Expand Down
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