I have run again the Bayesian algorithm that uses a prior near-ignorance model to compute US2016 election forecast.
This is the current situation for Clinton (worst-case in red and best-case in blue).
The probability range of winning the election (by getting the majority of the electoral votes) is [0.68,0.91]. The posterior distributions obtained using the
prior near-ignorance model are shown in figure:
The state by state situation is the following
The most uncertain states at the moment are