diff --git a/test/gibbs_example/gibbs.jl b/test/gibbs_example/gibbs.jl
index 817eb89..44f2481 100644
--- a/test/gibbs_example/gibbs.jl
+++ b/test/gibbs_example/gibbs.jl
@@ -1,36 +1,28 @@
+using AbstractMCMC, AbstractPPL
+using BangBang.ConstructorBase: ConstructorBase
+
 """
     Gibbs(sampler_map::NamedTuple)
 
-An interface for block sampling in Markov Chain Monte Carlo (MCMC).
-
-Gibbs sampling is a technique for dividing complex multivariate problems into simpler subproblems.
-It allows different sampling methods to be applied to different parameters.
+A Gibbs sampler that allows for block sampling using different inference algorithms for each parameter.
 """
-struct Gibbs{NT<:NamedTuple} <: AbstractMCMC.AbstractSampler
-    sampler_map::NT
+struct Gibbs{T<:NamedTuple} <: AbstractMCMC.AbstractSampler
+    sampler_map::T
 end
 
 struct GibbsState{TraceNT<:NamedTuple,StateNT<:NamedTuple,SizeNT<:NamedTuple}
-    """
-    Contains the values of all parameters up to the last iteration.
-    """
+    "Contains the values of all parameters up to the last iteration."
     trace::TraceNT
 
-    """
-    Maps parameters to their sampler-specific MCMC states.
-    """
+    "Maps parameters to their sampler-specific MCMC states."
     mcmc_states::StateNT
 
-    """
-    Maps parameters to their sizes.
-    """
+    "Maps parameters to their sizes."
     variable_sizes::SizeNT
 end
 
 struct GibbsTransition{ValuesNT<:NamedTuple}
-    """
-    Realizations of the parameters, this is considered a "sample" in the MCMC chain.
-    """
+    "Realizations of the parameters, this is considered a \"sample\" in the MCMC chain."
     values::ValuesNT
 end
 
@@ -95,7 +87,7 @@ Update the trace with the values from the MCMC states of the sub-problems.
 function update_trace(trace::NamedTuple, gibbs_state::GibbsState)
     for parameter_variable in keys(gibbs_state.mcmc_states)
         sub_state = gibbs_state.mcmc_states[parameter_variable]
-        sub_state_params = vec(sub_state)
+        sub_state_params = Base.vec(sub_state)
         unflattened_sub_state_params = unflatten(
             sub_state_params,
             NamedTuple{(parameter_variable,)}((
@@ -115,21 +107,19 @@ function AbstractMCMC.step(
     initial_params::NamedTuple,
     kwargs...,
 )
-    if Set(keys(initial_params)) != Set(sampler.parameter_names)
+    if Set(keys(initial_params)) != Set(keys(sampler.sampler_map))
         throw(
             ArgumentError(
-                "initial_params must contain all parameters in the model, expected $(sampler.parameter_names), got $(keys(initial_params))",
+                "initial_params must contain all parameters in the model, expected $(keys(sampler.sampler_map)), got $(keys(initial_params))",
             ),
         )
     end
 
-    mcmc_states = Dict{Symbol,Any}()
-    variable_sizes = Dict{Symbol,Tuple}()
-    for parameter_variable in sampler.parameter_names
+    mcmc_states, variable_sizes = map(keys(sampler.sampler_map)) do parameter_variable
         sub_sampler = sampler.sampler_map[parameter_variable]
 
         variables_to_be_conditioned_on = setdiff(
-            sampler.parameter_names, (parameter_variable,)
+            keys(sampler.sampler_map), (parameter_variable,)
         )
         conditioning_variables_values = NamedTuple{Tuple(variables_to_be_conditioned_on)}(
             Tuple([initial_params[g] for g in variables_to_be_conditioned_on])
@@ -141,7 +131,6 @@ function AbstractMCMC.step(
         # LogDensityProblems' `logdensity` function expects a single vector of real numbers
         # `Gibbs` stores the parameters as a named tuple, thus we need to flatten the sub_problem_parameters_values
         # and unflatten after the sampling step
-        variable_sizes[parameter_variable] = Tuple(size(initial_params[parameter_variable]))
         flattened_sub_problem_parameters_values = flatten(sub_problem_parameters_values)
 
         sub_state = last(
@@ -158,11 +147,13 @@ function AbstractMCMC.step(
                 kwargs...,
             ),
         )
-        mcmc_states[parameter_variable] = sub_state
+        (sub_state, Tuple(size(initial_params[parameter_variable])))
     end
 
     gibbs_state = GibbsState(
-        initial_params, NamedTuple(mcmc_states), NamedTuple(variable_sizes)
+        initial_params,
+        NamedTuple{Tuple(keys(sampler.sampler_map))}(mcmc_states),
+        NamedTuple{Tuple(keys(sampler.sampler_map))}(variable_sizes),
     )
     trace = update_trace(NamedTuple(), gibbs_state)
     return GibbsTransition(trace), gibbs_state
@@ -176,14 +167,9 @@ function AbstractMCMC.step(
     args...;
     kwargs...,
 )
-    trace = gibbs_state.trace
-    mcmc_states = gibbs_state.mcmc_states
-    variable_sizes = gibbs_state.variable_sizes
+    (; trace, mcmc_states, variable_sizes) = gibbs_state
 
-    mcmc_states_dict = Dict(
-        keys(mcmc_states) .=> [mcmc_states[k] for k in keys(mcmc_states)]
-    )
-    for parameter_variable in sampler.parameter_names
+    mcmc_states = map(keys(sampler.sampler_map)) do parameter_variable
         sub_sampler = sampler.sampler_map[parameter_variable]
         sub_state = mcmc_states[parameter_variable]
         variables_to_be_conditioned_on = setdiff(
@@ -196,7 +182,8 @@ function AbstractMCMC.step(
             logdensity_model.logdensity, conditioning_variables_values
         )
 
-        _, sub_state = AbstractMCMC.logdensity_and_state(cond_logdensity, sub_state)
+        logp = LogDensityProblems.logdensity_and_state(cond_logdensity, sub_state)
+        sub_state = constructorof(typeof(sub_state))(; logp=logp)
         sub_state = last(
             AbstractMCMC.step(
                 rng,
@@ -207,12 +194,10 @@ function AbstractMCMC.step(
                 kwargs...,
             ),
         )
-        mcmc_states_dict[parameter_variable] = sub_state
         trace = update_trace(trace, gibbs_state)
+        sub_state
     end
+    mcmc_states = NamedTuple{Tuple(keys(sampler.sampler_map))}(mcmc_states)
 
-    mcmc_states = NamedTuple{Tuple(keys(mcmc_states_dict))}(
-        Tuple([mcmc_states_dict[k] for k in keys(mcmc_states_dict)])
-    )
     return GibbsTransition(trace), GibbsState(trace, mcmc_states, variable_sizes)
 end
diff --git a/test/gibbs_example/mh.jl b/test/gibbs_example/mh.jl
index fae0361..24f1522 100644
--- a/test/gibbs_example/mh.jl
+++ b/test/gibbs_example/mh.jl
@@ -24,12 +24,12 @@ function Base.vec(state::MHState)
     return state.params
 end
 
-struct RandomWalkMH <: AbstractMCMC.AbstractSampler
-    σ::Float64
+struct RandomWalkMH{T} <: AbstractMCMC.AbstractSampler
+    σ::T
 end
 
-struct IndependentMH <: AbstractMCMC.AbstractSampler
-    proposal_dist::Distributions.Distribution
+struct IndependentMH{T} <: AbstractMCMC.AbstractSampler
+    proposal_dist::T
 end
 
 function AbstractMCMC.step(