I still don’t have great intuition for quickly spotting my bottlenecks due to unneeded allocations and the associated work around. Here is some code I’m trying to optimize right now:

```
function logpost(w,data,μ,Σ)
post = 0.0
# rightWrong = data[:,1]
# total = data[:,2]
# xvec = [1.0,0.0]
for (idx,d) in enumerate(data[:,2])
#xvec[2] = d # = [d^x for x in [0,1] ]
#display(xvec)
#μ = log(1 + exp(-w' * xvec)) # This is playing the role of the likelihood.
if data[idx,1] < 1e-2
@timeit "calc post1" post -= log(exp(w' * [1.0,d]) + 1)
else
@timeit "calc post2" post += w' * [1.0,d] - log(exp(w' * [1.0,d]) + 1)
end
@timeit "calc post3" post += logPriorGaussian(w,μ,Σ)
end
return post
end
function getDraws(post::Function, data::Array{Float64},ndraws::Int64,candsig2::Array{Float64},μ::Array{Float64}, Σ )::Array{Float64}
w = Array{Float64}(undef,(ndraws,2))
w[1,:] = Float64[-8,10]
i = 2
draw = Float64[0]
tempw = Float64[-8,10]
accepted = [0,0]
while i <= ndraws
for j in 1:2 # Loop over number of parameters that we are estimating.
w[i,j] = w[i-1,j] # Set the next draw to be the previous one. It will stay this way unless we decide to accept a new draw.
cand = Normal(w[i-1,j],sqrt(candsig2[j])) # Center the envelope distribution on the current value of w
@timeit "Draw normal" draw = rand(cand,1)
@timeit "reassign" tempw[j] = w[i-1,j]
@timeit "denom" denom = post(tempw,data,μ,Σ)
tempw[j] = draw[1]
@timeit "numerator" numerator = post(tempw,data,μ,Σ)
tempw[j] = w[i-1,j]
@timeit "calc" r = numerator - denom
unif = Uniform(0,1)
unifdraw = log.(rand(unif,1))
if r >= 1.0
w[i,j] = draw[1]
tempw[j] = draw[1]
accepted[j] += 1
elseif r < 1.0 && unifdraw[1] < r
w[i,j] = draw[1]
tempw[j] = draw[1]
accepted[j] += 1
end
end
i += 1
end
println(accepted, " ", ndraws)
println("Acceptance percentage:", accepted[1]/ndraws, " ",accepted[2]/ndraws)
return w
end
```

(There are some functions that are called here that I haven’t included, but could if you want to see them.)

Thanks in advance for any insights