class_docstr: Normal distribution with parameters mu and sigma.
  
  Parameters
  ----------
  seed
      Random number generator seed for reproducibility.
  
  Examples
  --------
  
  >>> from river import proba
  
  >>> p = proba.Gaussian()
  >>> p.update(6)
  >>> p.update(7)
  
  >>> p
  𝒩(μ=6.500, σ=0.707)
  
  >>> p(6.5)
  0.564189
  
  >>> p.revert(7)
  >>> p
  𝒩(μ=6.000, σ=0.000)
class_docstr_tok: ["Normal","distribution","with","parameters","mu","and","sigma",".","Parameters","--","--","--","--","--","seed","Random","number","generator","seed","for","reproducibility",".","Examples","--","--","--","--",">",">",">","from","river","import","proba",">",">",">","p","=","proba.Gaussian","(",")",">",">",">","p.update","(","6",")",">",">",">","p.update","(","7",")",">",">",">","p","\ud835\udca9","(","\u03bc=6.500",",","\u03c3=0.707",")",">",">",">","p","(","6.5",")","0.564189",">",">",">","p.revert","(","7",")",">",">",">","p","\ud835\udca9","(","\u03bc=6.000",",","\u03c3=0.000",")"]
code: def __init__(self, seed=None):
      super().__init__(seed)
      self._var = stats.Var(ddof=1)
  
imports: from __future__ import annotations
  import math
  import numpy
  import pandas
  from scipy.stats import multivariate_normal
  from river import covariance, stats
  from river.proba import base
label_desc: Use image node_id 1 to create a new Gaussian object from inherited base classes: base with example: obj = Gaussian(seed)