I got the result like this figure (with prior distribution of Uniform[-100,100]). Does it mean that the posterior of this paramater is approaching zero?
If the paramater approaches to zero, the model constructed will make no sense. So when the paramater approaching to zero, halfnormal-distribution-like posterior (as is shown in the figure) arise, does it mean that sampling comes to error, or the likelihood, which is the hypothetical model in my study, is not reliable?
If the problem occurs in the likelihood, actually my target model is complex with 14-20 parameters, shall I choose other bayesian method to complete paramater estimation? And if I can find a bayesian method to estimate so many parameters? Thanks!
And this is the figure makes me confused.
And this is the source code, which is a preliminary simple model. I want to add the parameters, but as the parameter increases, sampling is difficult to converge. That is why I wonder to find a new bayesian method.
XF_model_PL = pm.Model() with XF_model_PL: A = pm.Uniform("A_2",lower = -100, upper = 100) Ea = pm.Uniform("Ea_2",lower = 0, upper = 100) m = pm.Normal('m',mu = 1, sd = 1) n = pm.Normal('n', mu = 1, sd = 1) sigma = pm.HalfNormal('sigma',sd = 1e-6) k_2 = A_2 * 1e3 * np.exp( -Ea_2 * 1e3 / R / T_Bottom[:79]) r_2 = k_2 * (p_H2_in[:79] ** m * p_CO2_in[:79] ** n) * (1 - (p_H2O_in[:79] ** 2 * p_CH4_in[:79]) / (p_H2_in[:79] ** 4 * p_CO2_in[:79] * keq_2[:79])) r_CH4_obs = - r_2 r_CH4_likelihood = pm.Normal('r_CH4_likelihood',mu = - r_2, sd = sigma, observed = r0_CH4[:79])