Speedup algorithm: error bounds

Recall the following m states model:

.

.

.

where

.

.

.

with probability matrix:

whose row sums are all zeros as in 0812.

We firstly compute m policy functions corresponding to each theta and then pick a

policy function according to the probability matrix.

We implement the same algorithm of error bounds as yesterday’s when we compute

m policy functions respectively.