diff --git a/EpiAware/docs/src/examples/getting_started.jl b/EpiAware/docs/src/examples/getting_started.jl
index d9e49bb6c..555145e1e 100644
--- a/EpiAware/docs/src/examples/getting_started.jl
+++ b/EpiAware/docs/src/examples/getting_started.jl
@@ -4,7 +4,8 @@
 using Markdown
 using InteractiveUtils
 
-# ╔═╡ 4680eed2-dbd8-11ee-17d6-2552317711c1
+# ╔═╡ c593a2a0-d7f5-11ee-0931-d9f65ae84a72
+# hideall
 let
     docs_dir = dirname(dirname(@__DIR__))
     pkg_dir = dirname(docs_dir)
@@ -12,11 +13,10 @@ let
     using Pkg: Pkg
     Pkg.activate(docs_dir)
     Pkg.develop(; path = pkg_dir)
-    Pkg.resolve()
     Pkg.instantiate()
-end
+end;
 
-# ╔═╡ 914b0cd7-86ca-4227-96cb-7cd887956833
+# ╔═╡ da479d8d-1312-4b98-b0af-5be52dffaf3f
 begin
     using EpiAware
     using Turing
@@ -27,21 +27,15 @@ begin
     using Statistics
     using DataFramesMeta
     using LinearAlgebra
-    using Pathfinder
     using Transducers
+end
 
-    Random.seed!(1)
-end;
-
-# ╔═╡ 7c22d80b-2d52-4935-b62d-cbc64437195c
+# ╔═╡ 3ebc8384-f73d-4597-83a7-07a3744fed61
 md"
 # Getting stated with `EpiAware`
 
 This tutorial introduces the basic functionality of `EpiAware`. `EpiAware` is a package for making inferences on epidemiological case/determined infection data using a model-based approach.
-"
 
-# ╔═╡ 683743e6-9426-4b95-994b-4a579aa2564d
-md"
 ## `EpiAware` models
 The models we consider are discrete-time $t = 1,\dots, T$ with a latent random process, $Z_t$ generating stochasticity in the number of new infections $I_t$ at each time step. Observations are treated as downstream random variables determined by the actual infections and a model of infection to observation delay.
 
@@ -74,7 +68,7 @@ An `EpiAware` model in code is created from three modular components:
 Where $g_s$ is a discrete generation interval. For this reason, even when not using a reproductive number approach directly, we ask for a generation interval.
 "
 
-# ╔═╡ 0b554dd9-79c7-44bc-9cbf-ea56439cb80d
+# ╔═╡ 5a0d5ab8-e985-4126-a1ac-58fe08beee38
 md"
 ## Random walk `LatentModel`
 
@@ -100,11 +94,11 @@ Z_0 &\sim \mathcal{N}(0,1),\\
 ```
 "
 
-# ╔═╡ 65540fb2-f97f-49dc-91f4-af26b803994e
+# ╔═╡ 56ae496b-0094-460b-89cb-526627991717
 rwp = EpiAware.RandomWalk(Normal(),
-    truncated(Normal(0.0, 0.02), 0.0, 0.5))
+    truncated(Normal(0.0, 0.02), 0.0, Inf))
 
-# ╔═╡ 3ed6eb84-d0e1-4f09-9fa0-4021d0f79f88
+# ╔═╡ 767beffd-1ef5-4e6c-9ac6-edb52e60fb44
 md"
 ## Direct infection `EpiModel`
 
@@ -117,71 +111,78 @@ As discussed above, we still ask for a defined generation interval, which can be
 
 "
 
-# ╔═╡ 0a532e52-8305-470a-8462-2aa023b724a2
+# ╔═╡ 9e43cbe3-94de-44fc-a788-b9c7adb34218
 truth_GI = Gamma(2, 5)
 
-# ╔═╡ ec24d355-0158-45e9-9584-ed89bbb17b31
+# ╔═╡ f067284f-a1a6-44a6-9b79-f8c2de447673
 md"
 The `EpiData` constructor performs double interval censoring to convert our _continuous_ estimate of the generation interval into a discretized version. We also implement right truncation using the keyword `D_gen`.
 "
 
-# ╔═╡ 8452c589-aceb-41b7-978e-918b83db58d3
+# ╔═╡ c0662d48-4b54-4b6d-8c91-ddf4b0e3aa43
 model_data = EpiData(truth_GI, D_gen = 10.0)
 
-# ╔═╡ 6028ccd2-428f-4737-9fa6-ab5bf17631bd
+# ╔═╡ fd72094f-1b95-4d07-a8b0-ef47dc560dfc
 md"
 We can supply a prior for the initial log_infections.
 "
 
-# ╔═╡ bd6714a0-6e70-4602-993e-12238e1f37f2
+# ╔═╡ 6639e66f-7725-4976-81b2-6472419d1a62
 log_I0_prior = Normal(log(100.0), 1.0)
 
-# ╔═╡ af012cc5-02ea-47f4-8545-cf54f2c6f6cc
+# ╔═╡ df5e59f8-3185-4bed-9cca-7c266df17cec
 md"
 And construct the `EpiModel`.
 "
 
-# ╔═╡ 18e51238-4038-4022-9b22-0e51ed51ea0a
+# ╔═╡ 6fbdd8e6-2323-4352-9185-1f31a9cf9012
 epi_model = DirectInfections(model_data, log_I0_prior)
 
-# ╔═╡ d75e7957-a020-46f6-8d40-e2ac1abb917f
+# ╔═╡ 5e62a50a-71f4-4902-b1c9-fdf51fe145fa
 md"
 
 
 ### Delayed Observations `ObservationModel`
 
-The observation model is a negative binomial distribution with mean `μ` and cluster factor `1 / r`. Delays are implemented
-as the action of a sparse kernel on the infections $I(t)$.
+The observation model is a negative binomial distribution parameterised with mean $\mu$ and 'successes' parameter $r$. The standard deviation _relative_ to the mean $\sigma_{\text{rel}} = \sigma / \mu$ for negative binomial observations is,
 
 ```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).
-\end{align}
+\sigma_{\text{rel}} =(1/\sqrt{\mu}) + (1 / \sqrt{r}).
+```
+It is standard to use a half-t distribution for standard deviation priors (e.g. as argued in this [paper](http://www.stat.columbia.edu/~gelman/research/published/taumain.pdf)); we specialise this to a Half-Normal prior and use an _a priori_ assumption that a typical observation fluctuation around the mean (when the mean is $\sim\mathcal{O}(10^2)$) would be 10%. This implies a standard deviation prior,
+```math
+1 / \sqrt{r} \sim \text{HalfNormal}\Big(0.1 ~\sqrt{{\pi \over 2}}\Big).
+```
+The $\sqrt{{\pi \over 2}}$ factor ensures the correct prior mean (see [here](https://en.wikipedia.org/wiki/Half-normal_distribution)).
+
+The expected observed cases are delayed infections. Delays are implemented as the action of a sparse kernel on the infections $I(t)$.
+
+```math
+y_t \sim \text{NegBinomial}\Big(\mu = \sum_{s\geq 0} K[t, t-s] I(s), r\Big). \\
 ```
 "
 
-# ╔═╡ 74771683-48cb-456f-8089-65c2fe2fdef2
+# ╔═╡ e813d547-6100-4c43-b84c-8cebe306bda8
 md"
 We also set up the inference to occur over 100 days.
 "
 
-# ╔═╡ cba7072f-aaaa-40fe-8e5f-f22d98fbdb30
+# ╔═╡ c7580ae6-0db5-448e-8b20-4dd6fcdb1ae0
 time_horizon = 100
 
-# ╔═╡ 6c39de90-4791-42f0-b863-228753618c8a
+# ╔═╡ 0aa3fcbd-0831-45b8-9a2c-7ffbabf5895f
 md"
 We choose a simple observation model where infections are observed 0, 1, 2, 3 days later with equal probability.
 "
 
-# ╔═╡ b4a420af-71e6-4094-9795-9544dd5f34a2
+# ╔═╡ 448669bc-99f4-4823-b15e-fcc9040ba31b
 obs_model = EpiAware.DelayObservations(
     fill(0.25, 4),
     time_horizon,
-    truncated(Gamma(5, 0.05 / 5), 1e-3, 0.2)
+    truncated(Normal(0, 0.1 * sqrt(pi) / sqrt(2)), 0.0, Inf)
 )
 
-# ╔═╡ 5b667f47-8c90-42ca-8253-998a3ad3878d
+# ╔═╡ e49713e8-4840-4083-8e3f-fc52d791be7b
 md"
 ## Generate cases from the `EpiAware` model
 
@@ -190,40 +191,40 @@ Having chosen an `EpiModel`, `LatentModel` and `ObservationModel`, we can implem
 By giving `missing` to the first argument, we indicate that case data will be _generated_ from the model rather than treated as fixed.
 "
 
-# ╔═╡ 12632c1c-b233-4990-9e7e-9add1bb9a8ee
+# ╔═╡ abeff860-58c3-4644-9325-66ffd4446b6d
 full_epi_aware_mdl = make_epi_aware(missing, time_horizon;
     epi_model = epi_model,
     latent_model = rwp,
     observation_model = obs_model)
 
-# ╔═╡ 031a55e8-617b-4da4-859c-94b660ec424d
+# ╔═╡ 821628fb-8044-48b0-aa4f-0b7b57a2f45a
 md"
 We choose some fixed parameters:
 - Initial incidence is 100.
 - In the direct infection model, the initial incidence and in the initial value of the random walk form a non-identifiable pair. Therefore, we fix $Z_0 = 0$.
 "
 
-# ╔═╡ 775c7295-a4ea-484e-9ad9-291df3f6ffe8
+# ╔═╡ 36b34fd2-2891-42ca-b5dc-abb482e516ee
 fixed_parameters = (rw_init = 0.0, init_incidence = log(100.0))
 
-# ╔═╡ 5ce1855e-9f9e-4feb-8df3-8ec6139ac943
+# ╔═╡ 0aadd9e3-7f91-4b45-9663-67d11335f0d0
 md"
 We fix these parameters using `fix`, and generate a random epidemic.
 "
 
-# ╔═╡ 5b86a63b-677c-4125-b0be-1527b73b91bd
+# ╔═╡ 7e0e6012-8648-4f84-a25a-8b0138c4b72a
 cond_generative_model = fix(full_epi_aware_mdl, fixed_parameters)
 
-# ╔═╡ 4a1fc7bb-82a0-4643-a18b-d331a31c1390
+# ╔═╡ b20c28be-7b07-410c-a33b-ea5ad6828c12
 random_epidemic = rand(cond_generative_model)
 
-# ╔═╡ e571e7b6-0e26-4855-ae90-05a18be6ff38
+# ╔═╡ d073e63b-62da-4743-ace0-78ef7806bc0b
 true_infections = generated_quantities(cond_generative_model, random_epidemic).I_t
 
-# ╔═╡ 62092c7f-ebe7-428e-baaa-65c34be52371
+# ╔═╡ a04f3c1b-7e11-4800-9c2a-9fc0021de6e7
 generated_obs = generated_quantities(cond_generative_model, random_epidemic).generated_y_t
 
-# ╔═╡ 88e8fb2c-38ce-4c68-88b9-c42f3fa6de13
+# ╔═╡ f68b4e41-ac5c-42cd-a8c2-8761d66f7543
 let
     plot(true_infections,
         label = "I_t",
@@ -233,7 +234,7 @@ let
     scatter!(generated_obs, lab = "generated cases")
 end
 
-# ╔═╡ 2f90bee6-067d-4267-beb9-356e4a4d714c
+# ╔═╡ b5bc8f05-b538-4abf-aa84-450bf2dff3d9
 md"
 ## Inference
 Fixing $Z_0 = 0$ for the random walk was based on inference principles; in this model $Z_0$ and $\log I_0$ are non-identifiable.
@@ -243,19 +244,34 @@ However, we now treat the generated data as `truth_data` and make inference with
 We do the inference by MCMC/NUTS using the `Turing` NUTS sampler with default warm-up steps.
 "
 
-# ╔═╡ 7e48a4c5-cd30-4377-8a98-e0c23f2dc31e
+# ╔═╡ 4a4c6e91-8d8f-4bbf-bb7e-a36dc281e312
+md"
+The observation model supports partially complete data. To test this we set some of the generated observations to be `missing`.
+"
+
+# ╔═╡ 525aa98c-d0e5-4ffa-b808-d90fc986204c
 truth_data = generated_obs
 
-# ╔═╡ 272e6798-1151-486f-9667-924dbc63bd69
+# ╔═╡ 259a7042-e74f-43c7-aeb4-97a3beeb7776
+let
+    truth_data = Union{Int, Missing}[truth_data...]
+    truth_data[vcat([3, 5], 10:20)] .= missing
+end
+
+# ╔═╡ 32638954-2c99-4d4e-8e03-52154030c657
+md"
+We now make the model but fixing the initial condition of the random walk to be 0.
+"
+
+# ╔═╡ b4033728-b321-4100-8194-1fd9fe2d268d
+
 inference_mdl = fix(
-    make_epi_aware(truth_data, time_horizon;
-        epi_model = epi_model,
-        latent_model = rwp,
-        observation_model = obs_model),
+    make_epi_aware(truth_data, time_horizon; epi_model = epi_model,
+        latent_model = rwp, observation_model = obs_model),
     (rw_init = 0.0,)
 )
 
-# ╔═╡ 4298f0ec-f6df-42ee-aa28-f7ed60f1e530
+# ╔═╡ 9222b436-9445-4039-abbf-25c8cddb7f63
 md"
 ### Initialising inference
 
@@ -266,38 +282,35 @@ To make NUTS more robust we provide `manypathfinder`, which is built on pathfind
 `manypathfinder` differs from `Pathfinder.multipathfinder`; `multipathfinder` is aimed at sampling from a potentially non-Gaussian target distribution which is first approximated as a uniformly weighted collection of normal approximations from pathfinder runs. `manypathfinder` is aimed at moving rapidly to a 'good' part of parameter space, and is robust to runs that fail.
 "
 
-# ╔═╡ 40ebd47a-4a08-4a46-a727-26347d3fca51
+# ╔═╡ 197a4fbb-b71a-475a-bb78-28ff613e3094
 best_pf = manypathfinder(inference_mdl, 10; nruns = 20, executor = Transducers.ThreadedEx());
 
-# ╔═╡ b7d9a56a-b2d5-4595-a6b9-9cd5fa6b1445
+# ╔═╡ 073a1d40-456a-450e-969f-11b23eb7fd1f
 md"
 We can use draws from the best pathfinder run to initialise NUTS.
 "
 
-# ╔═╡ cdd805e2-b00c-4522-9261-1819c6a195eb
+# ╔═╡ 0379b058-4c35-440a-bc01-aafa0178bdbf
 best_pf.draws_transformed
 
-# ╔═╡ e847b0b6-9d70-46ba-bec6-1e3fa676a33c
+# ╔═╡ a7798f71-9bb5-4506-9476-0cc11553b9e2
 init_params = collect.(eachrow(best_pf.draws_transformed.value[1:4, :, 1]))
 
-# ╔═╡ 9734a535-e3d8-4481-9897-f537ad095d21
+# ╔═╡ 4deb3a51-781d-48c4-91f6-6adf2b1affcf
 md"
 **NB: We are running this inference run for speed rather than accuracy as a demonstration. Use a higher target acceptance and more samples in a typical workflow.**
 "
 
-# ╔═╡ 2fdb4ca6-47ba-4a16-95fa-14b2b32cef10
-begin
-    target_acc_rate = 0.8
-    chn = sample(inference_mdl,
-        NUTS(target_acc_rate; adtype = AutoReverseDiff(true)),
-        MCMCThreads(),
-        250,
-        4;
-        init_params,
-        drop_warmup = true)
-end
+# ╔═╡ 3eb5ec5e-aae7-478e-84fb-80f2e9f85b4c
+chn = sample(inference_mdl,
+    NUTS(; adtype = AutoReverseDiff(true)),
+    MCMCThreads(),
+    250,
+    4;
+    init_params,
+    drop_warmup = true)
 
-# ╔═╡ 2e42cb30-b087-4ae1-9b8f-95d103e1c290
+# ╔═╡ 30498cc7-16a5-441a-b8cd-c19b220c60c1
 md"
 ### Predictive plotting
 
@@ -306,7 +319,7 @@ We can spaghetti plot generated case data from the version of the model _which h
 Because we are using synthetic data we can also plot the model predictions for the _unobserved_ infections and check that (at least in this example) we were able to capture some unobserved/latent variables in the process accurate.
 "
 
-# ╔═╡ e74fc652-cd5f-4764-a416-caa8bab0bf0c
+# ╔═╡ e9df22b8-8e4d-4ab7-91ea-c01f2239b3e5
 let
     post_check_mdl = fix(full_epi_aware_mdl, (rw_init = 0.0,))
     post_check_y_t = mapreduce(hcat, generated_quantities(post_check_mdl, chn)) do gen
@@ -340,12 +353,12 @@ let
         size = (700, 400))
 end
 
-# ╔═╡ 96df9c68-b2e2-4669-b420-5ef23c77aee7
+# ╔═╡ 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.
 "
 
-# ╔═╡ 04d741d8-a2ff-48eb-90b1-e4da416eb582
+# ╔═╡ 10d8fe24-83a6-47ac-97b7-a374481473d3
 let
     parameters_to_plot = (:σ²_RW, :neg_bin_cluster_factor)
 
@@ -363,7 +376,7 @@ let
     plot(plts..., layout = (2, 1))
 end
 
-# ╔═╡ 42763332-096d-40eb-a152-96e858992ed4
+# ╔═╡ 81efe8ca-b753-4a12-bafc-a887a999377b
 md"
 ## Reproductive number back-calculation
 
@@ -372,7 +385,7 @@ As mentioned at the top, we _don't_ directly use the concept of reproductive num
 Here we spaghetti plot posterior sampled time-varying reproductive numbers against the actual.
 "
 
-# ╔═╡ 3b5a3fa6-fc57-4b3c-b03d-04641bf0e48b
+# ╔═╡ 15b9f37f-8d5f-460d-8c28-d7f2271fd099
 let
     n = epi_model.data.len_gen_int
     Rt_denom = [dot(reverse(epi_model.data.gen_int), true_infections[(t - n):(t - 1)])
@@ -397,48 +410,50 @@ let
 end
 
 # ╔═╡ Cell order:
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-# ╟─914b0cd7-86ca-4227-96cb-7cd887956833
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-# ╠═42763332-096d-40eb-a152-96e858992ed4
-# ╠═3b5a3fa6-fc57-4b3c-b03d-04641bf0e48b
+# ╟─c593a2a0-d7f5-11ee-0931-d9f65ae84a72
+# ╟─3ebc8384-f73d-4597-83a7-07a3744fed61
+# ╠═da479d8d-1312-4b98-b0af-5be52dffaf3f
+# ╟─5a0d5ab8-e985-4126-a1ac-58fe08beee38
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+# ╟─767beffd-1ef5-4e6c-9ac6-edb52e60fb44
+# ╠═9e43cbe3-94de-44fc-a788-b9c7adb34218
+# ╟─f067284f-a1a6-44a6-9b79-f8c2de447673
+# ╠═c0662d48-4b54-4b6d-8c91-ddf4b0e3aa43
+# ╟─fd72094f-1b95-4d07-a8b0-ef47dc560dfc
+# ╠═6639e66f-7725-4976-81b2-6472419d1a62
+# ╟─df5e59f8-3185-4bed-9cca-7c266df17cec
+# ╠═6fbdd8e6-2323-4352-9185-1f31a9cf9012
+# ╟─5e62a50a-71f4-4902-b1c9-fdf51fe145fa
+# ╟─e813d547-6100-4c43-b84c-8cebe306bda8
+# ╠═c7580ae6-0db5-448e-8b20-4dd6fcdb1ae0
+# ╟─0aa3fcbd-0831-45b8-9a2c-7ffbabf5895f
+# ╠═448669bc-99f4-4823-b15e-fcc9040ba31b
+# ╟─e49713e8-4840-4083-8e3f-fc52d791be7b
+# ╠═abeff860-58c3-4644-9325-66ffd4446b6d
+# ╟─821628fb-8044-48b0-aa4f-0b7b57a2f45a
+# ╠═36b34fd2-2891-42ca-b5dc-abb482e516ee
+# ╟─0aadd9e3-7f91-4b45-9663-67d11335f0d0
+# ╠═7e0e6012-8648-4f84-a25a-8b0138c4b72a
+# ╠═b20c28be-7b07-410c-a33b-ea5ad6828c12
+# ╠═d073e63b-62da-4743-ace0-78ef7806bc0b
+# ╠═a04f3c1b-7e11-4800-9c2a-9fc0021de6e7
+# ╟─f68b4e41-ac5c-42cd-a8c2-8761d66f7543
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+# ╟─4a4c6e91-8d8f-4bbf-bb7e-a36dc281e312
+# ╠═525aa98c-d0e5-4ffa-b808-d90fc986204c
+# ╠═259a7042-e74f-43c7-aeb4-97a3beeb7776
+# ╟─32638954-2c99-4d4e-8e03-52154030c657
+# ╠═b4033728-b321-4100-8194-1fd9fe2d268d
+# ╟─9222b436-9445-4039-abbf-25c8cddb7f63
+# ╠═197a4fbb-b71a-475a-bb78-28ff613e3094
+# ╠═073a1d40-456a-450e-969f-11b23eb7fd1f
+# ╠═0379b058-4c35-440a-bc01-aafa0178bdbf
+# ╠═a7798f71-9bb5-4506-9476-0cc11553b9e2
+# ╟─4deb3a51-781d-48c4-91f6-6adf2b1affcf
+# ╠═3eb5ec5e-aae7-478e-84fb-80f2e9f85b4c
+# ╟─30498cc7-16a5-441a-b8cd-c19b220c60c1
+# ╠═e9df22b8-8e4d-4ab7-91ea-c01f2239b3e5
+# ╟─fd6321b1-4c3a-4123-b0dc-c45b951e0b80
+# ╠═10d8fe24-83a6-47ac-97b7-a374481473d3
+# ╟─81efe8ca-b753-4a12-bafc-a887a999377b
+# ╠═15b9f37f-8d5f-460d-8c28-d7f2271fd099
diff --git a/EpiAware/src/inference-methods.jl b/EpiAware/src/inference-methods.jl
index e105390cf..8218d8de7 100644
--- a/EpiAware/src/inference-methods.jl
+++ b/EpiAware/src/inference-methods.jl
@@ -95,8 +95,7 @@ largest ELBO estimate.
 - `best_pfs::PathfinderResult`: Best pathfinder result by estimated ELBO.
 """
 function manypathfinder(mdl::DynamicPPL.Model, ndraws; nruns = 4,
-        nchains = 4, maxiters = 50, max_tries = 100, kwargs...)
-    ndraws = max(ndraws, nchains)
+        maxiters = 50, max_tries = 100, kwargs...)
     _run_manypathfinder(mdl; nruns, ndraws, maxiters, kwargs...) |>
     pfs -> _continue_manypathfinder!(pfs, mdl; max_tries, nruns, kwargs...) |>
            pfs -> _get_best_elbo_pathfinder(pfs)
diff --git a/EpiAware/src/observation-models.jl b/EpiAware/src/observation-models.jl
index 39b2b322d..d1599ef93 100644
--- a/EpiAware/src/observation-models.jl
+++ b/EpiAware/src/observation-models.jl
@@ -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,
@@ -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
 
diff --git a/EpiAware/test/test_inference-methods.jl b/EpiAware/test/test_inference-methods.jl
index c7f10e186..4a36b7aba 100644
--- a/EpiAware/test/test_inference-methods.jl
+++ b/EpiAware/test/test_inference-methods.jl
@@ -111,7 +111,7 @@ end
         maxiters = 50
         max_tries = 100
 
-        best_pf = manypathfinder(mdl, ndraws; nruns = nruns, nchains = nchains,
+        best_pf = manypathfinder(mdl, ndraws; nruns = nruns,
             maxiters = maxiters, max_tries = max_tries)
 
         @test best_pf isa PathfinderResult
@@ -128,7 +128,7 @@ end
         maxiters = 50
         max_tries = 10
 
-        best_pf = manypathfinder(mdl, ndraws; nruns = nruns, nchains = nchains,
+        best_pf = manypathfinder(mdl, ndraws; nruns = nruns,
             maxiters = maxiters, max_tries = max_tries)
 
         pathfinder_samples = best_pf.draws |> vec
diff --git a/EpiAware/test/test_observation-models.jl b/EpiAware/test/test_observation-models.jl
index 66b33531b..df6b4f7c9 100644
--- a/EpiAware/test/test_observation-models.jl
+++ b/EpiAware/test/test_observation-models.jl
@@ -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