How to customize von-mises-fisher distribution in PyMC3?

More information (what you have tried, etc) would help. In general, you want to wrap the logp in potential as a start.

Thank you for your reply. I have implemented the logp function, but since this distribution is defined on the unit sphere instead of the Cartesian coordinate system, I don’t know how to customize this distribution when estimating the hidden variables using the MCMC method. Here is my code.

def bessel(v, kappa):
    return iv(v, kappa)

def _get_vmf_likelihood_term(value, mu, kappa):
    return tt.exp(kappa *, value))

def _get_vmf_normalization_numerator(p, kappa):
    return kappa ** (0.5 * p - 1)

def _get_vmf_normalization_denom(p, kappa):
    return (2 * pi) ** (0.5 * p) * bessel(0.5 * p - 1, kappa)

def vmf_log_pdf(value, mu, kappa, C):
    likelihood = _get_vmf_likelihood_term(value, mu, kappa)
    normalization_numerator = _get_vmf_normalization_numerator(C, kappa)
    normalization_denominator = _get_vmf_normalization_denom(C, kappa)
    return tt.log(likelihood) + tt.log(normalization_numerator) - tt.log(normalization_denominator)

class vMF(pm.Continuous):
    def __init__(self, mu, kappa, C, *args, **kwargs):
        shape = np.atleast_1d(mu.shape)[-1]
        kwargs.setdefault("shape", shape)
        super(vMF, self).__init__(*args, **kwargs)
        self.mu_arr = mu = mu = tt.as_tensor_variable(mu)
        self.kappa = kappa
        self.C = C
    def logp(self, value):
        value = tt.as_tensor_variable(value)
        mu =
        kappa = self.kappa
        C = self.C
        return vmf_log_pdf(value, mu, kappa, C)

You can try passing a circular transformation to the class __init__ similar to VonMises distribution