add entry to history, bump version

Author: nsiccha

HISTORY.md

@@ -1,5 +1,12 @@
 # AdvancedHMC Changelog
 
+## 0.8.4
+
+  - Introduces an experimental way to improve the *diagonal* mass matrix adaptation using gradient information (similar to [nutpie](https://github.com/pymc-devs/nutpie)),
+      currently to be initialized for a `metric` of type `DiagEuclideanMetric`
+      via `mma = AdvancedHMC.NutpieVar(size(metric); var=copy(metric.M⁻¹))`
+      until a new interface is introduced in an upcoming breaking release to specify the method of adaptation.
+
 ## 0.8.0
 
   - To make an MCMC transtion from phasepoint `z` using trajectory `τ`(or HMCKernel `κ`) under Hamiltonian `h`, use `transition(h, τ, z)` or `transition(rng, h, τ, z)`(if using HMCKernel, use `transition(h, κ, z)` or `transition(rng, h, κ, z)`).

Project.toml

@@ -1,6 +1,6 @@
 name = "AdvancedHMC"
 uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
-version = "0.8.3"
+version = "0.8.4"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

docs/src/api.md

@@ -31,15 +31,15 @@ where `ϵ` is the step size of leapfrog integration.
 ### Adaptor (`adaptor`)
 
   - Adapt the mass matrix `metric` of the Hamiltonian dynamics: `mma = MassMatrixAdaptor(metric)`
-    
+
       + This is lowered to `UnitMassMatrix`, `WelfordVar` or `WelfordCov` based on the type of the mass matrix `metric`
-      + There is an experimental way to improve the *diagonal* mass matrix adaptation using gradient information (similar to [nutpie](https://github.com/pymc-devs/nutpie)), 
+      + There is an experimental way to improve the *diagonal* mass matrix adaptation using gradient information (similar to [nutpie](https://github.com/pymc-devs/nutpie)),
       currently to be initialized for a `metric` of type `DiagEuclideanMetric`
-      via `mma = AdvancedHMC.Adaptation.NutpieVar(size(metric); var=copy(metric.M⁻¹))` 
-      until a new interface is introduced to specify the method of adaptation.
+      via `mma = AdvancedHMC.NutpieVar(size(metric); var=copy(metric.M⁻¹))`
+      until a new interface is introduced in an upcoming breaking release to specify the method of adaptation.
 
   - Adapt the step size of the leapfrog integrator `integrator`: `ssa = StepSizeAdaptor(δ, integrator)`
-    
+
       + It uses Nesterov's dual averaging with `δ` as the target acceptance rate.
   - Combine the two above *naively*: `NaiveHMCAdaptor(mma, ssa)`
   - Combine the first two using Stan's windowed adaptation: `StanHMCAdaptor(mma, ssa)`
@@ -64,12 +64,12 @@ sample(
 Draw `n_samples` samples using the kernel `κ` under the Hamiltonian system `h`
 
   - The randomness is controlled by `rng`.
-    
+
       + If `rng` is not provided, the default random number generator (`Random.default_rng()`) will be used.
 
   - The initial point is given by `θ`.
   - The adaptor is set by `adaptor`, for which the default is no adaptation.
-    
+
       + It will perform `n_adapts` steps of adaptation, for which the default is `1_000` or 10% of `n_samples`, whichever is lower.
   - `drop_warmup` specifies whether to drop samples.
   - `verbose` controls the verbosity.