Update changelog, use -ise in prose

Author: penelopeysm

HISTORY.md

@@ -4,11 +4,13 @@
 
 An extension for MarginalLogDensities.jl has been added.
 
-Loading DynamicPPL and MarginalLogDensities now provides the `DynamicPPL.marginalize` function to marginalize out variables from a model.
+Loading DynamicPPL and MarginalLogDensities now provides the `DynamicPPL.marginalize` function to marginalise out variables from a model.
 This is useful for averaging out random effects or nuisance parameters while improving inference on fixed effects/parameters of interest.
 The `marginalize` function returns a `MarginalLogDensities.MarginalLogDensity`, a function-like callable struct that returns the approximate log-density of a subset of the parameters after integrating out the rest of them.
-By default, this uses the Laplace approximation and sparse AD, making the marginalization computationally very efficient.
-Please see [the documentation](https://turinglang.org/DynamicPPL.jl/v0.37/api/#Marginalization) for further information.
+By default, this uses the Laplace approximation and sparse AD, making the marginalisation computationally very efficient.
+Note that the Laplace approximation relies on the model being differentiable with respect to the marginalised variables, and that their posteriors are unimodal and approximately Gaussian.
+
+Please see [the MarginalLogDensities documentation](https://eloceanografo.github.io/MarginalLogDensities.jl/stable) and the [new Marginalisation section of the DynamicPPL documentation](https://turinglang.org/DynamicPPL.jl/v0.37/api/#Marginalisation) for further information.
 
 ## 0.37.3
 

docs/src/api.md

@@ -136,16 +136,16 @@ When using `predict` with `MCMCChains.Chains`, you can control which variables a
   - `include_all=false` (default): Include only newly predicted variables
   - `include_all=true`: Include both parameters from the original chain and predicted variables
 
-## Marginalization
+## Marginalisation
 
-DynamicPPL provides the `marginalize` function to marginalize out variables from a model.
+DynamicPPL provides the `marginalize` function to marginalise out variables from a model.
 This requires `MarginalLogDensities.jl` to be loaded in your environment.
 
 ```@docs
 marginalize
 ```
 
-A `MarginalLogDensity` object acts as a function which maps non-marginalized parameter values to a marginal log-probability.
+A `MarginalLogDensity` object acts as a function which maps non-marginalised parameter values to a marginal log-probability.
 To retrieve a VarInfo object from it, you can use:
 
 ```@docs