class_docstr: This is the Sparse Variational GP using MCMC (SGPMC).
  
  The key reference is :cite:t:`hensman2015mcmc`.
  
  The latent function values are represented by centered
  (whitened) variables, so
  
  .. math::
     :nowrap:
  
     \begin{align}
     \mathbf v & \sim N(0, \mathbf I) \\
     \mathbf u &= \mathbf L\mathbf v
     \end{align}
  
  with
  
  .. math::
      \mathbf L \mathbf L^\top = \mathbf K
docstr: Xnew is a data matrix of the points at which we want to predict
  
  This method computes
  
      p(F* | (U=LV) )
  
  where F* are points on the GP at Xnew, F=LV are points on the GP at Z,
docstr_tok: ["Xnew","is","a","data","matrix","of","the","points","at","which","we","want","to","predict","This","method","computes","p","(","F","*","|","(","U=LV",")",")","where","F","*","are","points","on","the","GP","at","Xnew",",","F=LV","are","points","on","the","GP","at","Z",","]
code: def predict_f(
      self,
      Xnew: InputData,
      full_cov: bool = False,
      full_output_cov: bool = False,
  ) -> MeanAndVariance:
      """
      Xnew is a data matrix of the points at which we want to predict
  
      This method computes
  
          p(F* | (U=LV) )
  
      where F* are points on the GP at Xnew, F=LV are points on the GP at Z,
  
      """
      mu, var = conditional(
          Xnew,
          self.inducing_variable,
          self.kernel,
          self.V,
          full_cov=full_cov,
          q_sqrt=None,
          white=True,
          full_output_cov=full_output_cov,
      )
      return mu + self.mean_function(Xnew), var
  
imports: from typing import Optional
  import numpy
  import tensorflow
  import tensorflow_probability
  from check_shapes import check_shapes, inherit_check_shapes
  from ..base import InputData, MeanAndVariance, Parameter, RegressionData
  from ..conditionals import conditional
  from ..kernels import Kernel
  from ..likelihoods import Likelihood
  from ..mean_functions import MeanFunction
  from ..utilities import to_default_float
  from .model import GPModel
  from .training_mixins import InternalDataTrainingLossMixin
  from .util import InducingPointsLike, data_input_to_tensor, inducingpoint_wrapper
label_desc: Use image node_id 6 for calling the SGPMC obj's underlying member method code with example usage: obj.predict_f(Xnew, full_cov, full_output_cov) and returns: unknown,  var