Slice step method

        From Neal 2003 (doi:10.1214/aos/1056562461)

def step(self):
        """
        Slice step method

        From Neal 2003 (doi:10.1214/aos/1056562461)
        """
        logy = self.loglike - rexponential(1)

        L = self.stochastic.value - runiform(0, self.w)
        R = L + self.w

        if self.doubling:
            # Doubling procedure
            K = self.m
            while (K and (logy < self.fll(L) or logy < self.fll(R))):
                if random() < 0.5:
                    L -= R - L
                else:
                    R += R - L
                K -= 1
        else:
            # Stepping out procedure
            J = np.floor(runiform(0, self.m))
            K = (self.m - 1) - J
            while(J > 0 and logy < self.fll(L)):
                L -= self.w
                J -= 1
            while(K > 0 and logy < self.fll(R)):
                R += self.w
                K -= 1

        # Shrinkage procedure
        self.stochastic.value = runiform(L, R)
        try:
            logy_new = self.loglike
        except ZeroProbability:
            logy_new = -np.infty
        while(logy_new < logy):
            if (self.stochastic.value < self.stochastic.last_value):
                L = float(self.stochastic.value)
            else:
                R = float(self.stochastic.value)
            self.stochastic.revert()
            self.stochastic.value = runiform(L, R)
            try:
                logy_new = self.loglike
            except ZeroProbability:
                logy_new = -np.infty