diff --git a/REQUIRE b/REQUIRE
index 072a5ba5b..f94ed7e98 100644
--- a/REQUIRE
+++ b/REQUIRE
@@ -6,7 +6,6 @@ Optim 0.16.0
 Distributions 0.16.4
 StatsBase 0.25.0
 KernelDensityEstimate 0.4.0
-HDF5 0.10.2
 JLD2 0.1.2
 FileIO 1.0.2
 ProgressMeter 0.6.0
diff --git a/examples/ApproximateConvolution.jl b/examples/ApproximateConvolution.jl
index 50c3e2ce6..90f34ff58 100644
--- a/examples/ApproximateConvolution.jl
+++ b/examples/ApproximateConvolution.jl
@@ -10,7 +10,7 @@ import IncrementalInference: getSample
 toggleYTicks()
 
 
-type MultiModalConditional <: IncrementalInference.FunctorPairwise
+mutable struct MultiModalConditional <: IncrementalInference.FunctorPairwise
   x::Vector{Distribution}
   hypo::Categorical
   MultiModalConditional{D <: Distribution}(x::Vector{D}, p::Categorical) = new(x, p)
diff --git a/src/FactorGraph01.jl b/src/FactorGraph01.jl
index bf9675435..6109dae44 100644
--- a/src/FactorGraph01.jl
+++ b/src/FactorGraph01.jl
@@ -151,7 +151,7 @@ function setDefaultNodeData!(v::Graphs.ExVertex,
     # dims = size(initval,1) # rows indicate dimensions
     sp = Int[0;] #round.(Int,range(dodims,stop=dodims+dims-1,length=dims))
     gbw = getBW(pN)[:,1]
-    gbw2 = Array{Float64}(length(gbw),1)
+    gbw2 = Array{Float64}(undef, length(gbw),1)
     gbw2[:,1] = gbw[:]
     pNpts = getPoints(pN)
     data = VariableNodeData(initval, stdev, pNpts,
@@ -299,7 +299,7 @@ function getVal(vA::Array{Graphs.ExVertex,1})
   cols = cumsum(cols)
   sc = cols[end]
   rw = floor(Int,rows[1])
-  val = Array{Float64,2}(rw, sc)
+  val = Array{Float64,2}(undef,rw, sc)
   for i in 1:(len-1)
       val[:,(cols[i]+1):cols[i+1]] = vals[i]
   end
diff --git a/src/IncrementalInference.jl b/src/IncrementalInference.jl
index a38701cd7..2a551e0fe 100644
--- a/src/IncrementalInference.jl
+++ b/src/IncrementalInference.jl
@@ -16,7 +16,6 @@ using
   Random,
   NLsolve,
   StatsBase,
-  HDF5,
   JLD2,
   FileIO,
   ProgressMeter,
@@ -27,7 +26,7 @@ using
 const KDE = KernelDensityEstimate
 
 import Base: convert
-import HDF5: root
+# import HDF5: root
 import Distributions: sample
 import Random: rand, rand!
 import KernelDensityEstimate: kde!
diff --git a/src/SolveTree01.jl b/src/SolveTree01.jl
index 001f28b94..b643f8bc2 100644
--- a/src/SolveTree01.jl
+++ b/src/SolveTree01.jl
@@ -263,7 +263,7 @@ function productbelief(fg::FactorGraph,
     pGM = getPoints(dens[1])
   else
     @warn "Unknown density product on vertid=$(vertid), lennonp=$(lennonp), lenpart=$(lenpart)"
-    pGM = Array{Float64,2}(0,1)
+    pGM = Array{Float64,2}(undef, 0,1)
   end
 
   return pGM