diff --git a/src/DynamicPPL.jl b/src/DynamicPPL.jl
index eaf30feed..015317798 100644
--- a/src/DynamicPPL.jl
+++ b/src/DynamicPPL.jl
@@ -66,6 +66,7 @@ export  AbstractVarInfo,
         Model,
         getmissings,
         getargnames,
+        generated_quantities,
 # Samplers
         Sampler,
         SampleFromPrior,
diff --git a/src/model.jl b/src/model.jl
index 739772b02..0a2b00606 100644
--- a/src/model.jl
+++ b/src/model.jl
@@ -200,3 +200,71 @@ function Distributions.loglikelihood(model::Model, varinfo::AbstractVarInfo)
     model(varinfo, SampleFromPrior(), LikelihoodContext())
     return getlogp(varinfo)
 end
+
+"""
+    generated_quantities(model::Model, chain::AbstractChains)
+
+Execute `model` for each of the samples in `chain` and return an array of the values
+returned by the `model` for each sample.
+
+# Examples
+## General
+Often you might have additional quantities computed inside the model that you want to
+inspect, e.g.
+```julia
+@model function demo(x)
+    # sample and observe
+    θ ~ Prior()
+    x ~ Likelihood()
+    return interesting_quantity(θ, x)
+end
+m = demo(data)
+chain = sample(m, alg, n)
+# To inspect the `interesting_quantity(θ, x)` where `θ` is replaced by samples
+# from the posterior/`chain`:
+generated_quantities(m, chain) # <= results in a `Vector` of returned values
+                               #    from `interesting_quantity(θ, x)`
+```
+## Concrete (and simple)
+```julia
+julia> using DynamicPPL, Turing
+
+julia> @model function demo(xs)
+           s ~ InverseGamma(2, 3)
+           m_shifted ~ Normal(10, √s)
+           m = m_shifted - 10
+
+           for i in eachindex(xs)
+               xs[i] ~ Normal(m, √s)
+           end
+
+           return (m, )
+       end
+demo (generic function with 1 method)
+
+julia> model = demo(randn(10));
+
+julia> chain = sample(model, MH(), 10);
+
+julia> generated_quantities(model, chain)
+10×1 Array{Tuple{Float64},2}:
+ (2.1964758025119338,)
+ (2.1964758025119338,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.09270081916291417,)
+ (0.043088571494005024,)
+ (-0.16489786710222099,)
+ (-0.16489786710222099,)
+```
+"""
+function generated_quantities(model::Model, chain::AbstractChains)
+    varinfo = VarInfo(model)
+    iters = Iterators.product(1:size(chain, 1), 1:size(chain, 3))
+    return map(iters) do (sample_idx, chain_idx)
+        setval!(varinfo, chain, sample_idx, chain_idx)
+        model(varinfo)
+    end
+end
diff --git a/src/varinfo.jl b/src/varinfo.jl
index 2d2351cea..1ccdac660 100644
--- a/src/varinfo.jl
+++ b/src/varinfo.jl
@@ -1178,9 +1178,12 @@ _setval!(vi::TypedVarInfo, values, keys) = _typed_setval!(vi, vi.metadata, value
 end
 
 function _setval_kernel!(vi::AbstractVarInfo, vn::VarName, values, keys)
-    sym = Symbol(vn)
-    regex = Regex("^$sym\$|^$sym\\[")
-    indices = findall(x -> match(regex, string(x)) !== nothing, keys)
+    string_vn = string(vn)
+    string_vn_indexing = string_vn * "["
+    indices = findall(keys) do x
+        string_x = string(x)
+        return string_x == string_vn || startswith(string_x, string_vn_indexing)
+    end
     if !isempty(indices)
         sorted_indices = sort!(indices; by=i -> string(keys[i]), lt=NaturalSort.natural)
         val = mapreduce(vcat, sorted_indices) do i
diff --git a/test/model.jl b/test/model.jl
index c00b2a95a..83899769a 100644
--- a/test/model.jl
+++ b/test/model.jl
@@ -44,4 +44,98 @@ Random.seed!(1234)
             end
         end
     end
+
+    @testset "setval! & generated_quantities" begin
+        @model function demo1(xs, ::Type{TV} = Vector{Float64}) where {TV}
+            m = TV(undef, 2)
+            for i in 1:2
+                m[i] ~ Normal(0, 1)
+            end
+
+            for i in eachindex(xs)
+                xs[i] ~ Normal(m[1], 1.)
+            end
+
+            return (m, )
+        end
+
+        @model function demo2(xs)
+            m ~ MvNormal(2, 1.)
+            
+            for i in eachindex(xs)
+                xs[i] ~ Normal(m[1], 1.)
+            end
+
+            return (m, )
+        end
+        
+        xs = randn(3);
+        model1 = demo1(xs);
+        model2 = demo2(xs);
+
+        chain1 = sample(model1, MH(), 100);
+        chain2 = sample(model2, MH(), 100);
+
+        res11 = generated_quantities(model1, chain1)
+        res21 = generated_quantities(model2, chain1)
+
+        res12 = generated_quantities(model1, chain2)
+        res22 = generated_quantities(model2, chain2)
+
+        # Check that the two different models produce the same values for
+        # the same chains.
+        @test all(res11 .== res21)
+        @test all(res12 .== res22)
+        # Ensure that they're not all the same (some can be, because rejected samples)
+        @test any(res12[1:end - 1] .!= res12[2:end])
+
+        test_setval!(model1, chain1)
+        test_setval!(model2, chain2)
+
+        # Next level
+        @model function demo3(xs, ::Type{TV} = Vector{Float64}) where {TV}
+            m = Vector{TV}(undef, 2)
+            for i = 1:length(m)
+                m[i] ~ MvNormal(2, 1.)
+            end
+            
+            for i in eachindex(xs)
+                xs[i] ~ Normal(m[1][1], 1.)
+            end
+
+            return (m, )
+        end
+
+        @model function demo4(xs, ::Type{TV} = Vector{Vector{Float64}}) where {TV}
+            m = TV(undef, 2)
+            for i = 1:length(m)
+                m[i] ~ MvNormal(2, 1.)
+            end
+            
+            for i in eachindex(xs)
+                xs[i] ~ Normal(m[1][1], 1.)
+            end
+
+            return (m, )
+        end
+
+        model3 = demo3(xs);
+        model4 = demo4(xs);
+        
+        chain3 = sample(model3, MH(), 100);
+        chain4 = sample(model4, MH(), 100);
+        
+        res33 = generated_quantities(model3, chain3)
+        res43 = generated_quantities(model4, chain3)
+        
+        res34 = generated_quantities(model3, chain4)
+        res44 = generated_quantities(model4, chain4)
+
+        # Check that the two different models produce the same values for
+        # the same chains.
+        @test all(res33 .== res43)
+        @test all(res34 .== res44)
+        # Ensure that they're not all the same (some can be, because rejected samples)
+        @test any(res34[1:end - 1] .!= res34[2:end])
+    end
 end
diff --git a/test/test_util.jl b/test/test_util.jl
index d8a926656..8bfad4d3d 100644
--- a/test/test_util.jl
+++ b/test/test_util.jl
@@ -36,3 +36,33 @@ function test_model_ad(model, logp_manual)
     @test y ≈ lp
     @test back(1)[1] ≈ grad
 end
+
+
+"""
+    test_setval!(model, chain; sample_idx = 1, chain_idx = 1)
+
+Test `setval!` on `model` and `chain`.
+
+Worth noting that this only supports models containing symbols of the forms
+`m`, `m[1]`, `m[1, 2]`, not `m[1][1]`, etc.
+"""
+function test_setval!(model, chain; sample_idx = 1, chain_idx = 1)
+    var_info = VarInfo(model)
+    spl = SampleFromPrior()
+    θ_old = var_info[spl]
+    DynamicPPL.setval!(var_info, chain, sample_idx, chain_idx)
+    θ_new = var_info[spl]
+    @test θ_old != θ_new
+    nt = DynamicPPL.tonamedtuple(var_info)
+    for (k, (vals, names)) in pairs(nt)
+        for (n, v) in zip(names, vals)
+            chain_val = if Symbol(n) ∉ keys(chain)
+                # Assume it's a group
+                vec(MCMCChains.group(chain, Symbol(n)).value[sample_idx, :, chain_idx])
+            else
+                chain[sample_idx, n, chain_idx]
+            end
+            @test v == chain_val
+        end
+    end
+end