code: def mcmc_emcee(self, n_walkers, n_run, n_burn, mean_start, sigma_start):
          
          sampler = emcee.EnsembleSampler(n_walkers, self.cosmoParam.numParam, self.chain.likelihood)
          p0 = emcee.utils.sample_ball(mean_start, sigma_start, n_walkers)
          new_pos, _, _, _ = sampler.run_mcmc(p0, n_burn)
          sampler.reset()
          store = InMemoryStorageUtil()
          for pos, prob, _, _ in sampler.sample(new_pos, iterations=n_run):
              store.persistSamplingValues(pos, prob, None)
          return store.samples
docs: returns the mcmc analysis of the parameter space