Adaptive proposals #39

arzwa · 2020-11-22T17:29:59Z

Hi, I implemented some very basic adaptive Metropolis-Hastings proposals that may be of interest for the AdvancedMH.jl package. These are basically simple symmetric random-walk proposals where the scale is subjected to diminishing adaptation (see e.g. section 3 here). These are mainly of interest in Metropolis-within-Gibbs-like algorithms, and they could be of interest in Turing. For instance, for a simple test case:

data = rand(Normal(2., 1.), 500)
m1 = DensityModel(x -> loglikelihood(Normal(x,1), data))
p1 = RandomWalkProposal(Normal())
p2 = AdaptiveProposal(Normal())
c1 = sample(m1, MetropolisHastings(p1), 10000; chain_type=Chains)
c2 = sample(m1, MetropolisHastings(p2), 10000; chain_type=Chains)

julia> ess(c1)
ESS
  parameters        ess      rhat 
      Symbol    Float64   Float64 

     param_1   605.4018    1.0013


julia> ess(c2)
ESS
  parameters         ess      rhat 
      Symbol     Float64   Float64 

     param_1   2222.6857    1.0006

Also, this is my first PR to a collaborative project, and am taking this as an opportunity to learn how to contribute to projects using git (hopefully with something useful). In other words, I'm not sure if I'm doing this right :)

If I find some time, and this is considered useful, I'd like to port some other related stuff I've used in other projects to AdvancedMH as well.

cpfiffer

Wow, what a lovely surprise to receive on a Sunday morning! This is great stuff -- really an excellent PR. It came with tests, the code is succinct and easy to review, and the feature is one I've wanted for a long time. Excellently done.

I left a handful of comments, but I think most of them are suggestions you should be able to commit directly by pressing "Commit suggestion". They're primarily style changes, but one docstring could use a little more info.

Thanks for the PR! I'm going to be really happy to have this merged in.

src/adaptive.jl

cpfiffer · 2020-11-22T18:28:47Z

src/adaptive.jl

+Adaptor(; tune=25, target=0.44, bound=10., δmax=0.2) =
+    Adaptor(0, 0, tune, target, bound, δmax)
+
+"""
+    AdaptiveProposal{P} 
+
+An adaptive Metropolis-Hastings proposal. In order for this to work, the
+proposal kernel should implement the `adapted(proposal, δ)` method, where `δ`
+is the increment/decrement applied to the scale of the proposal distribution
+during adaptation (e.g. for a Normal distribution the scale is `log(σ)`, so
+that after adaptation the proposal is `Normal(0, exp(log(σ) + δ))`).


Can you briefly describe the default Adaptor in the docstring for AdaptiveProposal, and notify everyone what the default keywords are for the adaptor?

src/adaptive.jl

Co-authored-by: Cameron Pfiffer <[email protected]>

devmotion

Thanks for the PR!

I left some additional comments.

devmotion · 2020-11-22T18:49:19Z

src/adaptive.jl

+mutable struct Adaptor
+    accepted::Integer
+    total   ::Integer
+    tune    ::Integer   # tuning interval


If possible one should avoid non-concrete fields:

Suggested change

mutable struct Adaptor

accepted::Integer

total ::Integer

tune ::Integer # tuning interval

mutable struct Adaptor

accepted::Int

total::Int

tune::Int # tuning interval

On a more general level, I'm not completely sure if it is useful to have a separate Adaptor struct, it seems it could just be integrated into AdaptiveProposal.

On an even more general level, I think it would be better to make this part of the state of the sampler using the AbstractMCMC interface instead of a field of the proposal. With the current design, the proposal will be mutated in every step. However, this (IMO preferred) design requires to implement AbstractMCMC.step instead of just adding the accept! call.

Yes of course, Int <-> Integer confusion...

The Adaptor struct may indeed be superfluous, although I found it a bit clearer separated that way. Also, I considered implementing adaptation for multivariate Normal proposals, which uses a different machinery under the hood, and my initial thought was to implement that as an AdaptiveProposal but with different Adaptor type. Of course, that could be implemented as another proposal struct altogether.

I think I understand conceptually your preferred design at the step level, although ATM my insight in how AbstractMCMC works is insufficient to see how that should be done, and currently, to me the mutation of the proposals is the most intuitive approach to implement adaptation. The accept! call seemed like a very simple, but admittedly somewhat hacky, way to enable adaptive proposals.

I think we should punt this problem to a later date. I would like to include accept/reject as a field in the Transition struct, which would make it very easy to count the number of previous acceptances by just adding adding one to the total_acceptances field in a Transition. Currently AdvancedMH doesn't track that internally, but I can just modify this code to remove the mutation later.

It's not only about the number of accepted/rejected steps here though, the state would have to include the updated proposal etc as well, so it won't be solved by including the stats in Transition.

However, I'm fine with postponing this refactoring. Probably best to open an issue so we don't forget it.

In my thinking, you'd add an extra field to Transitions that just accumulates the total number of acceptances, which is easier to get when you have individual acceptances for each draw. I'll open an issue up.

Yes, I understand (and I think that's a good addition). My point was just that it is not sufficient here.

I've opened an issue (#40).

devmotion · 2020-11-22T18:51:39Z

src/adaptive.jl

+"""
+mutable struct AdaptiveProposal{P} <: Proposal{P}
+    proposal::P
+    adaptor ::Adaptor


As written above, one might want to consider moving the fields from Adaptor here and maybe even better making it part of a state that is passed to the proposal in every sampling step. Also the updated proposal would be part of a separate state.

src/adaptive.jl

Co-authored-by: David Widmann <[email protected]>

…o adaptive

devmotion · 2020-11-23T09:42:22Z

The test failures seem to be real, the sampled values do not match the expected ones. As a side remark, probably it would be helpful for debugging to use sample(...; progress = false) in the tests.

arzwa · 2020-11-23T10:02:10Z

The test failures seem to be real, the sampled values do not match the expected ones. As a side remark, probably it would be helpful for debugging to use sample(...; progress = false) in the tests.

Where do you see this? As far as I can tell, the three failed runs show the following results:

Test Summary:                            | Pass  Fail  Error  Total
AdvancedMH                               |   20     2      1     23
  StaticMH                               |    4                   4
  RandomWalk                             |    4                   4
  Adaptive random walk                   |    4                   4
  Compare adaptive to simple random walk |    1                   1
  parallel sampling                      |    2     2             4
  Proposal styles                        |    4                   4
  Initial parameters                     |    1                   1
  EMCEE                                  |    4                   1

(which is the same as what I have locally). Maybe I'm looking in the wrong place.

devmotion · 2020-11-23T10:26:47Z

Where do you see this? As far as I can tell, the three failed runs show the following results:

I looked at the raw logs, e.g., https://pipelines.actions.githubusercontent.com/1iyxZmDwGspc6UbQKDLTZ8y3DHazaAGCEXSw02G4avGy4Eell5/_apis/pipelines/1/runs/7302/signedlogcontent/15?urlExpires=2020-11-23T10%3A22%3A22.7581222Z&urlSigningMethod=HMACV1&urlSignature=%2FUDiYzMH2mBCIhIroN0uueS%2FjYsHAv7zGwfFj%2FCie90%3D. The nicely formatted output of the Github Actions is truncated since progress bars are printed (which is why I suggested to use sample(...; progress = false)).

The raw logs are difficult to read but according to the final statement there are two test failures somewhere. And actually, if you scroll up a bit you can find


2020-11-23T07:54:54.1228900Z   Expression: ≈(mean(chain1["μ"]), 0.0, atol = 0.1)
2020-11-23T07:54:54.8851780Z    Evaluated: 1.9981419463187078 ≈ 0.0 (atol=0.1)
2020-11-23T07:54:54.8852673Z Stacktrace:
2020-11-23T07:54:54.8853787Z  [1] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9081715Z �[90m   @ �[39m�[90m~/work/AdvancedMH.jl/AdvancedMH.jl/test/�[39m�[90;4mruntests.jl:87�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9083060Z  [2] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9084504Z �[90m   @ �[39m�[90m/buildworker/worker/package_linux32/build/usr/share/julia/stdlib/v1.6/Test/src/�[39m�[90;4mTest.jl:1146�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9086203Z  [3] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9087509Z �[90m   @ �[39m�[90m~/work/AdvancedMH.jl/AdvancedMH.jl/test/�[39m�[90;4mruntests.jl:83�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9088694Z  [4] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9090192Z �[90m   @ �[39m�[90m/buildworker/worker/package_linux32/build/usr/share/julia/stdlib/v1.6/Test/src/�[39m�[90;4mTest.jl:1146�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9091420Z  [5] top-level scope
2020-11-23T07:54:54.9092587Z �[90m   @ �[39m�[90m~/work/AdvancedMH.jl/AdvancedMH.jl/test/�[39m�[90;4mruntests.jl:11�[0m
2020-11-23T07:54:55.3782818Z �[33m�[1m┌ �[22m�[39m�[33m�[1mWarning: �[22m�[39mOnly a single thread available: MCMC chains are not sampled in parallel
2020-11-23T07:54:55.3785177Z �[33m�[1m└ �[22m�[39m�[90m@ AbstractMCMC ~/.julia/packages/AbstractMCMC/lGdBK/src/sample.jl:204�[39m
2020-11-23T07:54:55.5398596Z �[32mSampling (1 threads)   0%|                              |  ETA: N/A�[39m
2020-11-23T07:54:56.5060198Z �[32mSampling (1 threads)  25%|███████▌                      |  ETA: 0:00:03�[39m
2020-11-23T07:54:56.5061792Z �[32mSampling (1 threads)  50%|███████████████               |  ETA: 0:00:01�[39m
2020-11-23T07:54:56.5063683Z �[32mSampling (1 threads)  75%|██████████████████████▌       |  ETA: 0:00:00�[39m
2020-11-23T07:54:56.5065281Z �[32mSampling (1 threads) 100%|██████████████████████████████| Time: 0:00:00�[39m
2020-11-23T07:54:56.5066874Z �[90mSampling (1 threads) 100%|██████████████████████████████| Time: 0:00:00�[39m
2020-11-23T07:54:56.5657764Z �[37mparallel sampling: �[39m�[91m�[1mTest Failed�[22m�[39m at �[39m�[1m/home/runner/work/AdvancedMH.jl/AdvancedMH.jl/test/runtests.jl:93�[22m
2020-11-23T07:54:56.5659855Z   Expression: ≈(mean(chain2["μ"]), 0.0, atol = 0.1)
2020-11-23T07:54:56.5865659Z    Evaluated: 1.9910977829961962 ≈ 0.0 (atol=0.1)

which I was referring to. Apparently the sample average of the samples of μ do not match the expected value 0 (and actually, differ quite significantly).

I just checked locally, and all tests pass with the latest release and with master. So it seems this PR breaks something.

test/runtests.jl

arzwa · 2020-11-23T10:43:53Z

Where do you see this? As far as I can tell, the three failed runs show the following results:

I looked at the raw logs, e.g., https://pipelines.actions.githubusercontent.com/1iyxZmDwGspc6UbQKDLTZ8y3DHazaAGCEXSw02G4avGy4Eell5/_apis/pipelines/1/runs/7302/signedlogcontent/15?urlExpires=2020-11-23T10%3A22%3A22.7581222Z&urlSigningMethod=HMACV1&urlSignature=%2FUDiYzMH2mBCIhIroN0uueS%2FjYsHAv7zGwfFj%2FCie90%3D. The nicely formatted output of the Github Actions is truncated since progress bars are printed (which is why I suggested to use sample(...; progress = false)).

The raw logs are difficult to read but according to the final statement there are two test failures somewhere. And actually, if you scroll up a bit you can find


2020-11-23T07:54:54.1228900Z   Expression: ≈(mean(chain1["μ"]), 0.0, atol = 0.1)
2020-11-23T07:54:54.8851780Z    Evaluated: 1.9981419463187078 ≈ 0.0 (atol=0.1)
2020-11-23T07:54:54.8852673Z Stacktrace:
2020-11-23T07:54:54.8853787Z  [1] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9081715Z �[90m   @ �[39m�[90m~/work/AdvancedMH.jl/AdvancedMH.jl/test/�[39m�[90;4mruntests.jl:87�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9083060Z  [2] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9084504Z �[90m   @ �[39m�[90m/buildworker/worker/package_linux32/build/usr/share/julia/stdlib/v1.6/Test/src/�[39m�[90;4mTest.jl:1146�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9086203Z  [3] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9087509Z �[90m   @ �[39m�[90m~/work/AdvancedMH.jl/AdvancedMH.jl/test/�[39m�[90;4mruntests.jl:83�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9088694Z  [4] �[0m�[1mmacro expansion�[22m
2020-11-23T07:54:54.9090192Z �[90m   @ �[39m�[90m/buildworker/worker/package_linux32/build/usr/share/julia/stdlib/v1.6/Test/src/�[39m�[90;4mTest.jl:1146�[0m�[90m [inlined]�[39m
2020-11-23T07:54:54.9091420Z  [5] top-level scope
2020-11-23T07:54:54.9092587Z �[90m   @ �[39m�[90m~/work/AdvancedMH.jl/AdvancedMH.jl/test/�[39m�[90;4mruntests.jl:11�[0m
2020-11-23T07:54:55.3782818Z �[33m�[1m┌ �[22m�[39m�[33m�[1mWarning: �[22m�[39mOnly a single thread available: MCMC chains are not sampled in parallel
2020-11-23T07:54:55.3785177Z �[33m�[1m└ �[22m�[39m�[90m@ AbstractMCMC ~/.julia/packages/AbstractMCMC/lGdBK/src/sample.jl:204�[39m
2020-11-23T07:54:55.5398596Z �[32mSampling (1 threads)   0%|                              |  ETA: N/A�[39m
2020-11-23T07:54:56.5060198Z �[32mSampling (1 threads)  25%|███████▌                      |  ETA: 0:00:03�[39m
2020-11-23T07:54:56.5061792Z �[32mSampling (1 threads)  50%|███████████████               |  ETA: 0:00:01�[39m
2020-11-23T07:54:56.5063683Z �[32mSampling (1 threads)  75%|██████████████████████▌       |  ETA: 0:00:00�[39m
2020-11-23T07:54:56.5065281Z �[32mSampling (1 threads) 100%|██████████████████████████████| Time: 0:00:00�[39m
2020-11-23T07:54:56.5066874Z �[90mSampling (1 threads) 100%|██████████████████████████████| Time: 0:00:00�[39m
2020-11-23T07:54:56.5657764Z �[37mparallel sampling: �[39m�[91m�[1mTest Failed�[22m�[39m at �[39m�[1m/home/runner/work/AdvancedMH.jl/AdvancedMH.jl/test/runtests.jl:93�[22m
2020-11-23T07:54:56.5659855Z   Expression: ≈(mean(chain2["μ"]), 0.0, atol = 0.1)
2020-11-23T07:54:56.5865659Z    Evaluated: 1.9910977829961962 ≈ 0.0 (atol=0.1)

which I was referring to. Apparently the sample average of the samples of μ do not match the expected value 0 (and actually, differ quite significantly).

I just checked locally, and all tests pass with the latest release and with master. So it seems this PR breaks something.

OK, thanks, I saw that too but was confused since I don't think I changed anything related to parallel sampling, and I thought you were referring to the AdaptiveProposal tests. I'll have a look.

Co-authored-by: David Widmann <[email protected]>

devmotion · 2020-11-23T10:54:25Z

test/runtests.jl

+    end
+
+    @testset "Compare adaptive to simple random walk" begin
+        data = rand(Normal(2., 1.), 500)


@arzwa This might be the problem - you redefine data and hence I assume you implicitly change the density model which is defined as a closure over data.

You could just rename the variable here but actually I think the better approach might be to "fix" the data in the model to avoid any such surprises in the future.

I guess this can be achieved by defining

density = let data = data θ -> insupport(θ) ? sum(logpdf.(dist(θ), data)) : -Inf end

But I haven't tested it, so make sure it actually fixes the problem 😄

Yes, I just saw this too, thanks. I'll check and push an updated test suite. (Actually, we could just as well test against the same data defined above in the test suite, but I find testing against a mean different from 0 a bit more reassuring since the sampler actually has to 'move' to somewhere from where it starts).

…o adaptive

devmotion · 2020-11-23T11:19:42Z

test/runtests.jl

+        kwargs = (progress=false, chain_type=StructArray, param_names=["μ", "σ"])
+        chain1 = sample(model, spl1, 100000; kwargs...)
+        chain2 = sample(model, spl2, 100000; kwargs...)


Can we maybe just define

_sample(args...; kwargs...) = sample(args...; progress = false, kwargs...)

at the top of the test suite and just replace all occurrences of sample with _sample? I think the kwargs definitions are a bit uncommon...

Sure, I often define a named tuple for kwargs that are repeatedly used, but could be an idiosyncratic thing :)

arzwa · 2020-12-08T15:39:27Z

Hi there, a question related to this PR (feel free to move it to the issue section, I'm not sure what the best place to drop this is).

I'm currently facing an application where I would really like to use adaptive proposals like those defined in this PR in a Metropolis-within-Gibbs setting (i.e. we have a parameter vector x, for each parameter have an adaptive univariate proposal, and in each iteration of the MCMC sampler we update each component of the parameter vector conditional on the others using a Metropolis-Hastings step). The Turing-way to go would seem to use the stuff implemented in AdvancedMH in a Turing composite Gibbs sampler (something roughly like Gibbs(AdaptiveMH(:p1), AdaptiveMH(:p2), ...) where the p1, p2, ... are the parameter vector components)? I think in general this is worthwhile for low-dimensional applications where the gradient of the loglikelihood is really costly or unavailable. I wonder what would be the best way to proceed to allow this? Thanks for any hints!

cpfiffer · 2020-12-08T16:12:21Z

Yeah, this should probably be a separate issue -- can you copy it over and I'll drop my comments there?

It looks like there's only one thing to change in this PR -- if that is updated I'd be happy to merge this one in.

arzwa · 2020-12-11T17:00:39Z

OK, I had implemented multivariate normal adaptive proposals and had it working so thought to add it to this PR. Not sure how the test pipeline got messed up?

devmotion · 2020-12-11T16:58:23Z

src/adaptive.jl

+# Adaptive proposals are only defined for symmetric proposal distributions
+is_symmetric_proposal(::AdaptiveProposal) = true


If this is the case, it would be good to check this property in an inner constructor.

devmotion · 2020-12-11T16:59:23Z

src/adaptive.jl

+function q(proposal::AdaptiveProposal, t, t_cond) 
+    return logpdf(proposal, t - t_cond)
+end
+


Is this actually needed? It seems like the default for Proposal should cover this.

Currently there seems to be no default q for Proposal. However it's also unnecessary because these adaptive proposals are always symmetric.

src/adaptivemvnormal.jl

devmotion · 2020-12-11T17:04:55Z

src/adaptivemvnormal.jl

+    F = cholesky(Hermitian(Σ), check=false) 
+    if rank(F.L) == p.d
+        p.adaptive = MvNormal(PDMat(Σ, F))
+    end


Also here, can't we just use the default constructors of MvNormal which call PDMat implicitly?

Suggested change

F = cholesky(Hermitian(Σ), check=false)

if rank(F.L) == p.d

p.adaptive = MvNormal(PDMat(Σ, F))

end

p.adaptive = MvNormal(Σ)

I implemented it this way (inspired by the Mamba implementation) to check whether the empirical estimate of the covariance matrix is a proper covariance matrix. If it is we can adapt the proposal distribution without additional computation by feeding the cholesky decomposition to MvNormal this way. How would you do it otherwise, I guess you could use try/catch but that doesn't seem nicer?

No, I don't think a try-catch block would be any better. IMO it is not good to just suppress the errors here and use an incorrect Cholesky decomposition with possibly negative eigenvalues. I would suggest the following:

If possible, update the Cholesky decomposition (or some other parametrization of the covariance matrix) iteratively. Usually this is more efficient than recomputing the factorization in every step and it avoid the numerical problems.

Often calling Matrix(Symmetric(Sigma)) is sufficient to avoid problems with matrices that are not completely symmetric and for which therefore the Cholesky decomposition fails.

Alternatively, use PositiveFactorizations.

IMO the first alternative would clearly be the best, if possible.

I just had a quick look at Haario et al. and the update formula of the covariance matrix in eq (3). It seems that indeed the update could be performed by only d + 3 rank 1 up- and downdates which each can be performed in O(d^2). Actually, if eps = 0, then only 3 rank 1 up- and downdates are needed and therefore the iterative update of the Cholesky decomposition would be much cheaper than recomputing the decomposition in O(d^3) operations. The rank 1 up- and downdates are implemented in LinearAlgebra.lowrankupdate! and LinearAlgebra.lowrankdowndate! (and their corresponding out-of-place methods).

I think this would be the preferable method for updating the Cholesky decomposition. Probably it would also be cheaper to save just the Cholesky decomposition and not wrap it in a MvNormal object since it is trivial to sample from the multivariate normal distribution with the Cholesky decomposition.

That sounds interesting and worthwhile, have to look at it. FWIW I just based my code on the description in the Roberts & Rosenthal paper and the Mamba implementation, since I simply wanted to use this for a problem I'm having.

I had the impression from the code that there is a preference to use Distributions.jl distributions for sampling, that's why I wrapped them in MvNormal objects instead of using the Cholesky factor directly (when both the full matrix and cholesky factors are available, it is as cheap to put them in an MvNormal). When updating the cholesky factor directly I agree it would be better to to do as you suggest.

src/adaptivemvnormal.jl

devmotion · 2020-12-11T17:06:04Z

src/adaptivemvnormal.jl

+function propose(rng::Random.AbstractRNG, proposal::AdaptiveMvNormal, m::DensityModel)
+    return rand(rng, proposal)
+end
+


Is this not covered by the default for Proposal?

src/mh-core.jl

test/Project.toml

devmotion · 2020-12-11T17:09:26Z

test/emcee.jl

@@ -18,7 +18,7 @@
            Random.seed!(100)
            sampler = Ensemble(1_000, StretchProposal([InverseGamma(2, 3), Normal(0, 1)]))
            chain = sample(model, sampler, 1_000;
-                           param_names = ["s", "m"], chain_type = Chains)
+                           param_names = ["s", "m"], chain_type = Chains, progress = false)


See https://github.com/TuringLang/AdvancedMH.jl/pull/39/files#r528631166.

Co-authored-by: David Widmann <[email protected]>

yebai · 2021-01-28T17:06:42Z

Anything missing from this PR? If there is nothing major standing in the way, maybe we should revisit and get it merged asap.

arzwa · 2021-01-28T17:25:39Z

Hi, I should have a look at it again, @devmotion had some good suggestions on the multivariate adaptive normal kernel, which I did not yet look into further at as I got busy with other stuff. The univariate adaptive proposals should be fine I think.

cnrrobertson · 2023-09-12T23:30:50Z

Is this still of interest/relevant? I'd definitely be interested in using this and may be able to help finish it up.

yebai · 2023-09-13T21:17:09Z

Is this still of interest/relevant? I'd definitely be interested in using this and may be able to help finish it up.

It's still relevant; please feel free to take this over.

arzwa added 4 commits November 22, 2020 16:59

adaptive proposal

e9fd602

Adaptive univariate proposals tests

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swp files from vim...

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test ESS

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arzwa changed the title ~~Adaptive~~ Adaptive proposals Nov 22, 2020

export AdaptiveProposal

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cpfiffer reviewed Nov 22, 2020

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arzwa and others added 9 commits November 22, 2020 19:58

Update src/adaptive.jl

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Co-authored-by: Cameron Pfiffer <[email protected]>

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devmotion reviewed Nov 22, 2020

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arzwa and others added 6 commits November 23, 2020 08:12

Update src/adaptive.jl

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Co-authored-by: David Widmann <[email protected]>

Update src/adaptive.jl

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Co-authored-by: David Widmann <[email protected]>

Update src/adaptive.jl

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test update

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Merge branch 'adaptive' of https://github.com/arzwa/AdvancedMH.jl int…

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…o adaptive

Adaptor docstring

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Update test/runtests.jl

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Co-authored-by: David Widmann <[email protected]>

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arzwa added 3 commits November 23, 2020 12:10

updated tests

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Merge branch 'adaptive' of https://github.com/arzwa/AdvancedMH.jl int…

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cpfiffer mentioned this pull request Nov 23, 2020

Tracking acceptances #40

Open

luiarthur mentioned this pull request Nov 23, 2020

Skipping the Hastings computation for symmetric proposals #41

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arzwa added 3 commits December 10, 2020 21:12

adaptive MvNormal

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merge with master

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AdaptiveMvNormal with tests

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arzwa and others added 7 commits December 11, 2020 18:26

Update src/adaptivemvnormal.jl

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Update src/adaptivemvnormal.jl

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Update src/adaptivemvnormal.jl

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Co-authored-by: David Widmann <[email protected]>

Update test/Project.toml

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Co-authored-by: David Widmann <[email protected]>

Update src/mh-core.jl

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Co-authored-by: David Widmann <[email protected]>

Update src/adaptivemvnormal.jl

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removed test Project.toml

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kaandocal mentioned this pull request May 26, 2021

Adaptive Metropolis algorithm #57

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cpfiffer mentioned this pull request Oct 25, 2022

Adaptive proposals in Turing composite Gibbs sampler #44

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torfjelde mentioned this pull request Dec 4, 2024

Implementation of Robust Adaptive Metropolis #106

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		# Adaptive proposals are only defined for symmetric proposal distributions
		is_symmetric_proposal(::AdaptiveProposal) = true

Adaptive proposals #39

Are you sure you want to change the base?

Adaptive proposals #39

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devmotion commented Nov 23, 2020

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arzwa commented Dec 8, 2020

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yebai commented Jan 28, 2021

arzwa commented Jan 28, 2021

cnrrobertson commented Sep 12, 2023

yebai commented Sep 13, 2023

arzwa Nov 22, 2020 •

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