b3388a99c35e7df5ebef0718838744ebf039c09c,pymc3/distributions/timeseries.py,GaussianRandomWalk,_random,#GaussianRandomWalk#Any#Any#Any#,252

Before Change

    def _random(self, sigma, mu, size):
        Implement a Gaussian random walk as a cumulative sum of normals.
        rv = stats.norm(mu, sigma)
        return rv.rvs(size).cumsum(axis=0)

    def _repr_latex_(self, name=None, dist=None):
        if dist is None:
            dist = self

After Change

    def _random(self, sigma, mu, size):
        Implement a Gaussian random walk as a cumulative sum of normals.
        rv = stats.norm(mu, sigma)
        data = rv.rvs(size).cumsum(axis=0)
        data = data - data[0]  // TODO: this should be a draw from `init`, if available
        return data

    def _repr_latex_(self, name=None, dist=None):

Instances