Apply multivariate normal log-likelihood find the posterior distribution

Questions
11

Is this something like what you need?

a = -1.2422069376608758
b = 0.010979502286122458
c = 0.005065108695132867
d = 0.0003998832861218647
e = 0.16015398278991752
f = 0.00013362145005939259
g = 2.0112165363058473e-06
h = 0.0020280971401678734
i = 4.0276945348026916e-05
j = 0.01657469140501759

with pm.Model():
    q0 = pm.Uniform('nodes 1', 0.054, 0.066)
    q1 = pm.Uniform('nodes2', 0.9, 1.1)
    q2 = pm.Uniform('nodes3', 45, 55)
    mu = a * q0**2 + b * q0 * q1+ c * q1**2- d * q0 * q2+ e * q0 - f * q1 * q2+ g * q2**2 - h * q1 - i * q2 + j
    pm.Normal('observed', mu, 0.1, observed=observed)
    trace = pm.sample()
    pm.traceplot(trace)