Clean up the docs

Author: ptiede

docs/src/index.md

@@ -9,8 +9,7 @@ The goal is to allow the user to easily combine and modify a set of primitive mo
 to construct complicated source structures. The benefit of this approach is that it is straightforward to construct different source models out of these primitives. Namely, an end-user does
 not have to create a separate source "model" every time they
 change the model specification. Additionally, most models currently implemented are differentiable with at Zygote and sometimes ForwardDiff[^2]. This allows for gradient accelerated optimization and sampling (e.g., HMC) to be used with little
-effort by the end user.
-To sample from the posterior, we provide a somewhat barebones interface since, most of the time, and we don't require the additional features offered by most PPLs. Additionally, the overhead introduced by PPLs tends to be rather large. In the future, we may revisit this as
+effort by the end user. To sample from the posterior, we provide a somewhat barebones interface since, most of the time, and we don't require the additional features offered by most PPLs. Additionally, the overhead introduced by PPLs tends to be rather large. In the future, we may revisit this as
 Julia's PPL ecosystem matures.
 
 !!! note
@@ -19,14 +18,17 @@ Julia's PPL ecosystem matures.
 
 ## Tutorials
 
+Our tutorial section currently has a large number of examples. The simplest example is fitting simple geometric models to the 2017 M87 data and is detailed in the [Geometric Modeling of EHT Data tutorial](@ref). We also include "non-parametric" modeling or imaging examples in [Imaging a Black Hole using only Closure Quantities](@ref), and [Stokes I Simultaneous Image and Instrument Modeling](@ref). There is also an introduction to hybrid geometric and image modeling in [Hybrid Imaging of a Black Hole](@ref), which combines physically motivated geometric modeling with the flexibility of image-based models. Finally, we
+provide a tutorial on how to use `Comrade` to model polarized data including simultaneously solving for 
+the image and instrumental effects like gain ratios and leakage terms in [Polarized Image and Instrumental Modeling](@ref).
+
+
 
 ## Contributing
 
 This repository has recently moved to [ColPrac](https://github.com/SciML/ColPrac). If you would like to contribute please feel free to open a issue or pull-request.
 
-[^2]: As of 0.9 Comrade switched to using full covariance closures. As a result this requires a sparse cholesky solve in the likelihood evaluation which requires 
-a Dual number overload. As a result we recommend using Zygote which does work and
-often is similarly performant (reverse 3-6x slower compared to the forward pass).
+[^2]: As of 0.9 Comrade switched to using full covariance closures. As a result this requires a sparse cholesky solve in the likelihood evaluation which requires which isn't defined in ForwardDiff. As a result we recommend using Zygote which does work and often is similarly performant (reverse pass is 3-6x slower compared to the forward pass).
 
 ## Requirements
 

lib/ComradeAHMC/src/ComradeAHMC.jl

@@ -115,6 +115,7 @@ To initialize the chain the user can set `initial_params` to `Vector{NamedTuple}
 elements are the starting locations for each of the `nchains`. If no starting location
 is specified `nchains` random samples from the prior will be chosen for the starting locations.
 
+
 For possible `kwargs` please see the [`AdvancedHMC.jl docs`](https://github.com/TuringLang/AdvancedHMC.jl)
 
 This returns a `PosteriorSamples` object indexed as iteration × chain.
@@ -234,8 +235,9 @@ elements are the starting locations for each of the `nchains`. If no starting lo
 is specified `nchains` random samples from the prior will be chosen for the starting locations.
 
 With `saveto` the user can optionally specify whether to store the samples in memory `MemoryStore`
-or save directly to disk with `DiskStore(filename, stride)`. The `stride` controls how often t
-he samples are dumped to disk.
+or save directly to disk with `DiskStore(filename, stride)`. The `stride` controls how often
+the samples are dumped to disk. In addition is saving to disk the user can also resume a
+previous run by setting `restart=true`.
 
 For possible `kwargs` please see the [`AdvancedHMC.jl docs`](https://github.com/TuringLang/AdvancedHMC.jl)