updated project version, fixed readme and tests to pass julia 1.0 tests

Author: Brian DePasquale

Project.toml

@@ -1,6 +1,6 @@
 name = "AdvancedMH"
 uuid = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170"
-version = "0.5.4"
+version = "0.5.5"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

README.md

@@ -123,7 +123,8 @@ chain = psample(model, RWMH(init_params), 100000, 4; param_names=["μ","σ"], ch
 
 AdvancedMH.jl also offers an implementation of [MALA](https://en.wikipedia.org/wiki/Metropolis-adjusted_Langevin_algorithm) if the `ForwardDiff` and `DiffResults` packages are available. 
 
-A set of `init_params` are required. `x->` within the `MALA` struct takes the gradient computed at the current sample.
+A `MALA` sampler can be constructed by `MALA(proposal)` where `proposal` is a function that
+takes the gradient computed at the current sample. It is required to specify an initial sample `init_params` when calling `sample`.
 
 ```julia
 # Import the package.
@@ -144,11 +145,9 @@ density(θ) = insupport(θ) ? sum(logpdf.(dist(θ), data)) : -Inf
 # Construct a DensityModel.
 model = DensityModel(density)
 
-#MvNormal covariance
-Sigma = 1e-2 * I(2)
-
-# Set up our sampler with a joint multivariate Normal proposal.
-spl = MALA(x-> MvNormal((1/2) * Sigma * x, Sigma))
+# Set up the sampler with a multivariate Gaussian proposal.
+sigma = 1e-1
+spl = MALA(x -> MvNormal((sigma^2 / 2) .* x, sigma))
 
 # Sample from the posterior.
 chain = sample(model, spl, 100000; init_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])

src/MALA.jl

@@ -24,6 +24,7 @@ transition(::MALA, model, params) = GradientTransition(model, params)
 # Store the new draw, its log density and its gradient
 GradientTransition(model::DensityModel, params) = GradientTransition(params, logdensity_and_gradient(model, params)...)
 
+propose(rng::Random.AbstractRNG, ::MALA, model) = error("please specify initial parameters")
 
 function propose(
     rng::Random.AbstractRNG,

test/runtests.jl

@@ -107,10 +107,9 @@ using ForwardDiff
 
     @testset "MALA" begin
 
-        Sigma = 1e-2 * I(2)
-        
-        # Set up our sampler with initial parameters.
-        spl1 = MALA(x-> MvNormal((1/2) * Sigma * x, Sigma))
+        # Set up the sampler.
+        sigma = 1e-1
+        spl1 = MALA(x -> MvNormal((sigma^2 / 2) .* x, sigma))
 
         # Sample from the posterior with initial parameters.
         chain1 = sample(model, spl1, 100000; init_params=ones(2), chain_type=StructArray, param_names=["μ", "σ"])