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stan的英文歌词 - 百度知道 stan的英文歌词StanEnimemChorus: DidoMy tea's gone cold I'm wondering why I got out of bed at allThe morning rain clouds up my window and I can't see at allAnd even if I could it'll a
bayesian - Example where the posterior from Jags and Stan are really . . . This isn't an answer, but I often find that methods I construct using JAGS have good Frequentist characteristics for the things I care about, and that the marginal distributions of the things I care about match what I get in STAN but are much faster to obtain The mixing on things I don't care about might be very bad in these situations I know from personal experience that (some on) the STAN
r - Divergent transitions in Stan - Cross Validated A divergent transition in Stan tells you that the region of the posterior distribution around that divergent transition is geometrically difficult to explore For example here is a quote from the manual: The primary cause of divergent transitions in Euclidean HMC (other than bugs in the code) is highly varying posterior curvature, for which small step sizes are too inefficient in some regions
r - brms intercept only model runs very slow - Cross Validated Under the hood, the brms package builds a Stan model There are two things that happen that take some time First, Stan compiles some C++ code After that, Stan runs an MCMC (Markov Chain Monte Carlo) algorithm that draws samples from the posterior distribution (The actual details are way more complicated than that, but I've tried to capture the essence in a nutshell ) Both steps can take