Unsuccessfull attempt at fixing SMC2

src/Control/Monad/Bayes/Inference/RMSMC.hs

@@ -51,8 +51,8 @@ rmsmc ::
   PopulationT m a
 rmsmc (MCMCConfig {..}) (SMCConfig {..}) =
   marginal
-    . S.sequentially (composeCopies numMCMCSteps (TrStat.hoist (single . flatten) . mhStep) . TrStat.hoist resampler) numSteps
-    . S.hoistFirst (TrStat.hoist (withParticles numParticles))
+    . S.sequentially (composeCopies numMCMCSteps (TrStat.hoistModel (single . flatten) . TrStat.hoist (single . flatten) . mhStep) . TrStat.hoist resampler) numSteps
+    . S.hoistFirst (TrStat.hoistModel (single . flatten) . TrStat.hoist (withParticles numParticles))
 
 -- | Resample-move Sequential Monte Carlo with a more efficient
 -- tracing representation.

src/Control/Monad/Bayes/Inference/SMC.hs

@@ -22,18 +22,38 @@ where
 
 import Control.Monad.Bayes.Class (MonadDistribution, MonadMeasure)
 import Control.Monad.Bayes.Population
-  ( PopulationT,
+  ( PopulationT (..),
+    flatten,
     pushEvidence,
+    single,
     withParticles,
   )
+import Control.Monad.Bayes.Population.Applicative qualified as Applicative
 import Control.Monad.Bayes.Sequential.Coroutine as Coroutine
+import Control.Monad.Bayes.Sequential.Coroutine qualified as SequentialT
+import Control.Monad.Bayes.Weighted (WeightedT (..), weightedT)
+import Control.Monad.Coroutine
+import Control.Monad.Trans.Free (FreeF (..), FreeT (..))
 
 data SMCConfig m = SMCConfig
   { resampler :: forall x. PopulationT m x -> PopulationT m x,
     numSteps :: Int,
     numParticles :: Int
   }
 
+sequentialToPopulation :: (Monad m) => Coroutine.SequentialT (Applicative.PopulationT m) a -> PopulationT m a
+sequentialToPopulation =
+  PopulationT
+    . weightedT
+    . coroutineToFree
+    . Coroutine.runSequentialT
+  where
+    coroutineToFree =
+      FreeT
+        . fmap (Free . fmap (\(cont, p) -> either (coroutineToFree . extract) (pure . (,p)) cont))
+        . Applicative.runPopulationT
+        . resume
+
 -- | Sequential importance resampling.
 -- Basically an SMC template that takes a custom resampler.
 smc ::
@@ -42,12 +62,15 @@ smc ::
   Coroutine.SequentialT (PopulationT m) a ->
   PopulationT m a
 smc SMCConfig {..} =
-  Coroutine.sequentially resampler numSteps
+  (single . flatten)
+    . Coroutine.sequentially resampler numSteps
+    . SequentialT.hoist (single . flatten)
     . Coroutine.hoistFirst (withParticles numParticles)
+    . SequentialT.hoist (single . flatten)
 
 -- | Sequential Monte Carlo with multinomial resampling at each timestep.
 -- Weights are normalized at each timestep and the total weight is pushed
 -- as a score into the transformed monad.
 smcPush ::
   (MonadMeasure m) => SMCConfig m -> Coroutine.SequentialT (PopulationT m) a -> PopulationT m a
-smcPush config = smc config {resampler = (pushEvidence . resampler config)}
+smcPush config = smc config {resampler = (single . flatten . pushEvidence . resampler config)}

src/Control/Monad/Bayes/Inference/SMC2.hs

@@ -27,8 +27,10 @@ import Control.Monad.Bayes.Class
 import Control.Monad.Bayes.Inference.MCMC
 import Control.Monad.Bayes.Inference.RMSMC (rmsmc)
 import Control.Monad.Bayes.Inference.SMC (SMCConfig (SMCConfig, numParticles, numSteps, resampler), smcPush)
-import Control.Monad.Bayes.Population as Pop (PopulationT, resampleMultinomial, runPopulationT)
+import Control.Monad.Bayes.Population as Pop (PopulationT, flatten, resampleMultinomial, runPopulationT, single)
+import Control.Monad.Bayes.Population qualified as PopulationT
 import Control.Monad.Bayes.Sequential.Coroutine (SequentialT)
+import Control.Monad.Bayes.Sequential.Coroutine qualified as SequentialT
 import Control.Monad.Bayes.Traced
 import Control.Monad.Trans (MonadTrans (..))
 import Numeric.Log (Log)
@@ -71,4 +73,10 @@ smc2 k n p t param m =
   rmsmc
     MCMCConfig {numMCMCSteps = t, proposal = SingleSiteMH, numBurnIn = 0}
     SMCConfig {numParticles = p, numSteps = k, resampler = resampleMultinomial}
-    (param >>= setup . runPopulationT . smcPush (SMCConfig {numSteps = k, numParticles = n, resampler = resampleMultinomial}) . m)
+    (flattenSequentiallyTraced param >>= setup . runPopulationT . smcPush (SMCConfig {numSteps = k, numParticles = n, resampler = resampleMultinomial}) . flattenSMC2 . m)
+
+flattenSequentiallyTraced :: (Monad m) => SequentialT (TracedT (PopulationT m)) a -> SequentialT (TracedT (PopulationT m)) a
+flattenSequentiallyTraced = SequentialT.hoist $ hoistModel (single . flatten) . hoist (single . flatten)
+
+flattenSMC2 :: (Monad m) => SequentialT (PopulationT (SMC2 m)) a -> SequentialT (PopulationT (SMC2 m)) a
+  flattenSMC2 = SequentialT.hoist $ single . flatten . PopulationT.hoist (SMC2 . flattenSequentiallyTraced . setup)

src/Control/Monad/Bayes/Population.hs

@@ -238,7 +238,7 @@ pushEvidence ::
   (MonadFactor m) =>
   PopulationT m a ->
   PopulationT m a
-pushEvidence = hoist applyWeight . extractEvidence
+pushEvidence = single . flatten . hoist applyWeight . extractEvidence
 
 -- | A properly weighted single sample, that is one picked at random according
 -- to the weights, with the sum of all weights.

src/Control/Monad/Bayes/Sequential/Coroutine.hs

@@ -22,6 +22,8 @@ module Control.Monad.Bayes.Sequential.Coroutine
     hoist,
     sequentially,
     sis,
+    runSequentialT,
+    extract,
   )
 where
 

src/Control/Monad/Bayes/Traced/Static.hs

@@ -12,6 +12,7 @@
 module Control.Monad.Bayes.Traced.Static
   ( TracedT (..),
     hoist,
+    hoistModel,
     marginal,
     mhStep,
     mh,
@@ -25,6 +26,7 @@ import Control.Monad.Bayes.Class
     MonadMeasure,
   )
 import Control.Monad.Bayes.Density.Free (DensityT)
+import Control.Monad.Bayes.Density.Free qualified as DensityT
 import Control.Monad.Bayes.Traced.Common
   ( Trace (..),
     bind,
@@ -33,6 +35,7 @@ import Control.Monad.Bayes.Traced.Common
     singleton,
   )
 import Control.Monad.Bayes.Weighted (WeightedT)
+import Control.Monad.Bayes.Weighted qualified as WeightedT
 import Control.Monad.Trans (MonadTrans (..))
 import Data.List.NonEmpty as NE (NonEmpty ((:|)), toList)
 
@@ -72,6 +75,9 @@ instance (MonadMeasure m) => MonadMeasure (TracedT m)
 hoist :: (forall x. m x -> m x) -> TracedT m a -> TracedT m a
 hoist f (TracedT m d) = TracedT m (f d)
 
+hoistModel :: (Monad m) => (forall x. m x -> m x) -> TracedT m a -> TracedT m a
+hoistModel f (TracedT m d) = TracedT (WeightedT.hoist (DensityT.hoist f) m) d
+
 -- | Discard the trace and supporting infrastructure.
 marginal :: (Monad m) => TracedT m a -> m a
 marginal (TracedT _ d) = fmap output d
@@ -98,15 +104,15 @@ mhStep (TracedT m d) = TracedT m d'
 -- * What is the probability that it is the weekend?
 --
 -- >>> :{
---  let
---    bus = do x <- bernoulli (2/7)
---             let rate = if x then 3 else 10
---             factor $ poissonPdf rate 4
---             return x
---    mhRunBusSingleObs = do
---      let nSamples = 2
---      sampleIOfixed $ unweighted $ mh nSamples bus
---  in mhRunBusSingleObs
+-- let
+--   bus = do x <- bernoulli (2/7)
+--            let rate = if x then 3 else 10
+--            factor $ poissonPdf rate 4
+--            return x
+--   mhRunBusSingleObs = do
+--     let nSamples = 2
+--     sampleIOfixed $ unweighted $ mh nSamples bus
+-- in mhRunBusSingleObs
 -- :}
 -- [True,True,True]
 --