%plot the mean of the posterior simulations and the 95% bounds

%posterior_sim_sens is a length(prior_changes) x MM x 3  page matrix. It
%records the MM sampled values from the posterior with c
%(=max(lambda_1+lambda_2) varying according to prior_changes

pc = length(prior_changes);

for j=1:pc
sort_post_lam_1(:,j) = sort(posterior_sim_sens(j,:,1));
end
x_post_lam_1 = mean(sort_post_lam_1)
l_post_lam_1(1:pc) = sort_post_lam_1((MM*.05),1:pc) 
u_post_lam_1(1:pc) = sort_post_lam_1((MM*.95),1:pc)
for j=1:pc
sort_post_lam_2(:,j) = sort(posterior_sim_sens(j,:,2));
end
x_post_lam_2 = mean(sort_post_lam_1)
l_post_lam_2(1:pc) = sort_post_lam_2((MM*.05), 1:pc) 
u_post_lam_2(1:pc) = sort_post_lam_2((MM*.95), 1:pc)

for j=1:pc
sort_post_al(:,j) = sort(posterior_sim_sens(j,:,3));
end

x_post_al = mean(sort_post_al)
l_post_al(1:pc) = sort_post_al((MM*.05), 1:pc) 
u_post_al(1:pc) = sort_post_al((MM*.95), 1:pc)