3 ms·
Right, but I mean that HMC has parameters to tune and multiples the computation needed per sample produced so it's not clear to me for which problems it's signi
by versteegen 2y ago
Right, but I mean that HMC has parameters to tune and multiples the computation needed per sample produced so it's not clear to me for which problems it's significantly beneficial and with how much tuning. I'd guess it's best where you otherwise don't have a good proposal distribution for Metropolis-Hastings leading to either many rejects or very slow mixing.
- shiandow 2y agoThe NUTS sampler limits how much tuning you need. As far as I know it's currently one of the best algorithms for sampling the kind of posterior probability distributions you get with Bayesian methods.