Timeline for Is it valid to perform Bayesian optimization and MCMC sampling with nested model parameters?

5 events

when toggle format	what		by	license	comment
Apr 16, 2021 at 17:29	answer	added	Xi'an		timeline score: 1
Apr 16, 2021 at 17:15	comment	added	Darcy		(a) Yes it should be log likelihood as you say. (b) $d_p = F(m)$ (i.e. in order to compute predicted data ($d_p$), I need to pass my model parameters through some function $F(m)$). (c) $m_1, m_2, m_3, ...$ are the unknown model parameters. As such $a, b, $ and $c$ are also unknown (and depend on $m$). I am trying to sample the posterior distribution (of e.g. $m$) given the likelihood (and some prior which at this point are just upper and lower limits on the modelled parameters).
Apr 16, 2021 at 17:09	history	edited	Darcy	CC BY-SA 4.0	added 4 characters in body
Apr 16, 2021 at 16:57	comment	added	Xi'an		(a) the $\chi^2$ expression is unlikely to be a likelihood function. Do you mean log- or negative log-likelihood? (b) I do not understand the distinction between $F(\mathcal m)$ and $\mathcal{d_p}$. (c) The model parameter is $\mathcal m$ and thus should be the one simulated, while $a,b,c$ are deterministic functions of $\mathcal m$. This seems unrelated with the use of MCMC or another simulation technique.
Apr 16, 2021 at 16:21	history	asked	Darcy	CC BY-SA 4.0