does Thompson sampling for price optimisation require discriminative pricing I get the gist of Thompson sampling for price optimisation (I think - see this video around minute 31). I wonder, would Thompson sampling require discriminative pricing or can prices be change sequentially during the day? Thanks.

If by discriminative pricing you mean

that n customers could encounter m different prices at a/the same point in time

then it's not a problem. You would sample the price for a batch of customers and given the outcomes, you would make the Bayesian update. For example, in the case of Bernoulli bandit, for a single customer, if no purchase was observed, the update would be $\mathsf{Beta}(\alpha, \beta+1)$, if you observed $m$ purchases and $k$ cases without purchase, the update would just be $\mathsf{Beta}(\alpha + m, \beta+k)$. The same applies to continuous rewards, you just need to make an update that considers multiple samples.
