diff --git a/Project.toml b/Project.toml
index ec3ffee..dd50daf 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "AffineInvariantMCMC"
 uuid = "a0f608ac-68d0-5108-ad9a-0cdca73324f3"
-version = "1.2.2"
+version = "1.2.3"
 
 [deps]
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
diff --git a/README.md b/README.md
index bdd0bab..cdbc899 100644
--- a/README.md
+++ b/README.md
@@ -73,11 +73,11 @@ Restarts
 
 AffineInvariantMCMC analyses can be performed utilizing extremely efficient restarts.
 
-Typically, the AffineInvariantMCMC runs require large number of functional (model) evaluations which may take substantial computational time.
+Typically, the AffineInvariantMCMC runs require a large number of functional (model) evaluations which may take substantial computational time.
 Occasionally, the AffineInvariantMCMC runs may crash due to external issues (e.g., network/computer/disk failures).
 Furthermore, AffineInvariantMCMC runs may require more time than the allowed allocation time on existing HPC cluster queues.
-In all these cases, the AffineInvariantMCMC runs needs to be restarted.
-Our codes allow are very efficient restarts with very minimal overhead and without re-execution of completed functional (model) evaluations.
+In all these cases, the AffineInvariantMCMC runs need to be restarted.
+Our codes allow for efficient restarts with very minimal overhead and without re-execution of completed functional (model) evaluations.
 
 For more information, check out our Julia module [ReusableFunctions](https://github.com/madsjulia/ReusableFunctions.jl).
 
diff --git a/src/AffineInvariantMCMC.jl b/src/AffineInvariantMCMC.jl
index a03d588..e13af49 100644
--- a/src/AffineInvariantMCMC.jl
+++ b/src/AffineInvariantMCMC.jl
@@ -46,6 +46,9 @@ function sample(llhood::Function, numwalkers::Integer, x0::AbstractMatrix{<:Real
 		chain, llhoodvals = JLD2.load(filename, "chain", "llhoods")
 	end
 	x = copy(x0)
+	if numsamples_perwalker < 2
+		numsamples_perwalker = 2
+	end
 	chain = Array{Float64}(undef, size(x0, 1), numwalkers, div(numsamples_perwalker, thinning))
 	lastllhoodvals = RobustPmap.rpmap(llhood, map(i->x[:, i], 1:size(x, 2)))
 	llhoodvals = Array{Float64}(undef, numwalkers, div(numsamples_perwalker, thinning))
@@ -94,4 +97,4 @@ function flattenmcmcarray(chain::AbstractArray, llhoodvals::AbstractArray)
 	return newchain, llhoodvals[1:end]
 end
 
-end
+end
\ No newline at end of file