Add changelog

Author: penelopeysm

HISTORY.md

@@ -2,7 +2,26 @@
 
 ## DynamicPPL 0.38
 
-Lorem ipsum dynamicppl sit amet
+Turing.jl v0.41 brings with it all the underlying changes in DynamicPPL 0.38.
+
+The only user-facing difference is that initial parameters for MCMC sampling must now be specified in a different form.
+You still need to use the `initial_params` keyword argument to `sample`, but the allowed values are different.
+For almost all samplers in Turing.jl (except `Emcee`) this should now be a `DynamicPPL.AbstractInitStrategy`.
+
+TODO LINK TO DPPL DOCS WHEN THIS IS LIVE
+
+There are three kinds of initialisation strategies provided out of the box with Turing.jl (they are exported so you can use these directly with `using Turing`):
+
+  - `InitFromPrior()`: Sample from the prior distribution. This is the default for most samplers in Turing.jl (if you don't specify `initial_params`).
+  - `InitFromUniform(a, b)`: Sample uniformly from `[a, b]` in linked space. This is the default for Hamiltonian samplers. If `a` and `b` are not specified it defaults to `[-2, 2]`, which preserves the behaviour in previous versions (and mimics that of Stan).
+  - `InitFromParams(p)`: Explicitly provide a set of initial parameters. **Note: `p` must be either a `NamedTuple` or a `Dict{<:VarName}`; it can no longer be a `Vector`.** Parameters must be provided in unlinked space, even if the sampler later performs linking.
+
+This change is made because Vectors are semantically ambiguous.
+It is not clear which element of the vector corresponds to which variable in the model, nor is it clear whether the parameters are in linked or unlinked space.
+Previously, both of these would depend on the internal structure of the VarInfo, which is an implementation detail.
+In contrast, the behaviour of `Dict`s and `NamedTuple`s is invariant to the ordering of variables and it is also easier for readers to understand which variable is being set to which value.
+
+If you were previously using `varinfo[:]` to extract a vector of initial parameters, you can now use `Dict(k => varinfo[k] for k in keys(varinfo)` to extract a Dict of initial parameters.
 
 ## Initial step in MCMC sampling