The bottleneck for sampling from the posterior is sampling v from the truncated normal distribution, which I’m doing using this implementation of minimax tilting rejection sampling. It’s not exactly instant, but on my laptopt it’s a few CPU-ms/sample up to ~100 comparisons and 50 CPU-ms up to ~200 comparisons. This probably prevents it from being useful for lists with many hundreds of items, but I’ve found it to be fine for interactive use since selecting the next comparison doesn’t require any sampling. The only thing which is prohibitively slow (for me at least) is computing the entropy of the full posterior since it involves so many evaluations of Φm.
The bottleneck for sampling from the posterior is sampling v from the truncated normal distribution, which I’m doing using this implementation of minimax tilting rejection sampling. It’s not exactly instant, but on my laptopt it’s a few CPU-ms/sample up to ~100 comparisons and 50 CPU-ms up to ~200 comparisons. This probably prevents it from being useful for lists with many hundreds of items, but I’ve found it to be fine for interactive use since selecting the next comparison doesn’t require any sampling. The only thing which is prohibitively slow (for me at least) is computing the entropy of the full posterior since it involves so many evaluations of Φm.