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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def sample_from_posterior(self, A: pd.DataFrame) -> None: """ Run Bayesian inference - sample from the posterior distribution."""

        self.sample_from_proposal(A)
        self.set_latent_state_sequence(A)
        self.update_log_prior(A)
        self.update_log_likelihood()

        candidate_log_joint_probability = self.log_prior + self.log_likelihood

        delta_log_joint_probability = (
            candidate_log_joint_probability - self.log_joint_probability
        )

        acceptance_probability = min(1, np.exp(delta_log_joint_probability))
        if acceptance_probability > np.random.rand():
            self.update_log_joint_probability()
        else:
            A[f"∂({self.source})/∂t"][self.target] = self.original_value
            self.set_latent_state_sequence(A)
            self.update_log_likelihood()
            self.update_log_prior(A)
            self.update_log_joint_probability()