How to get a smooth posterior? Using custom likelihood function

Hi, all.
I have a custom likelihood and define four priors:

reaction_9 = pm.Uniform(‘reaction_parameters9’, lower=-1, upper=1,initval=0)
Hm = pm.Uniform(‘H(m)’, lower=-1, upper=1,initval=0)
O__x = pm.Uniform(“O’'(ox)”, lower=-1, upper=1,initval=0)
Ox = pm.Uniform(‘Ox’, lower=-1, upper=1,initval=0)

Then I run:

trace = pm.sample(1000,step=pm.Metropolis())

pymc3 or pymc5 /pm.Metropolis() or pm.NUTS().Results are roughly the same:

My questions are:
1.Why my posterior distributions fluctuate so much? Is there any method to smooth them?
2.When I use pm.NUTS() to sample, I define the gradient of log_likelihood_function like this:

  def der_log_likelihood(theta, data):
      def lnlike(values):
      return current(values, data)

      eps = np.sqrt(np.finfo(float).eps)
      grads = scipy.optimize.approx_fprime(theta[0], lnlike, eps * np.ones(len(theta)))
   return grads

Is it ok?
3.My custom likelihood function contains for loops:

for i in range(len(test_potential)):
    test_potential[i] = test_potential[i] -0.5507
logp = logp-len(data) * np.log(np.sqrt(2.0 * np.pi) * sigma)

for k in range(len(test_potential)):
    Energy = test_potential[k]

When I use pm.NUTS() to sample, it’s too slow(8000draws cost 15+hours).Should I change for loops into theano.scan or wrap variables or something else to make sample faster?
4.I define the likelihood function like this:

logp += -np.sum((data[k]1000 - curr0.1)** 2.0) / (2.0 * sigma ** 2.0)

Suppose I have k observation data points(array obs) in different condition, and I get k simulation data points(array curr) from my model.
How to directly define the likelihood function?
I tried:

like = pm.Normal(‘like’,mu=curr, sigma=1, observed_data=obs)
It seems infeasible.

Thanks for any answer. It will be really helpful for me. :heart: