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Section Navigation

  • Distributions
    • Continuous
      • pymc.AsymmetricLaplace
      • pymc.Beta
      • pymc.Cauchy
      • pymc.ChiSquared
      • pymc.ExGaussian
      • pymc.Exponential
      • pymc.Flat
      • pymc.Gamma
      • pymc.Gumbel
      • pymc.HalfCauchy
      • pymc.HalfFlat
      • pymc.HalfNormal
      • pymc.HalfStudentT
      • pymc.Interpolated
      • pymc.InverseGamma
      • pymc.Kumaraswamy
      • pymc.Laplace
      • pymc.Logistic
      • pymc.LogitNormal
      • pymc.LogNormal
      • pymc.Moyal
      • pymc.Normal
      • pymc.Pareto
      • pymc.PolyaGamma
      • pymc.Rice
      • pymc.SkewNormal
      • pymc.SkewStudentT
      • pymc.StudentT
      • pymc.Triangular
      • pymc.TruncatedNormal
      • pymc.Uniform
      • pymc.VonMises
      • pymc.Wald
      • pymc.Weibull
    • Discrete
      • pymc.Bernoulli
      • pymc.BetaBinomial
      • pymc.Binomial
      • pymc.Categorical
      • pymc.DiscreteUniform
      • pymc.DiscreteWeibull
      • pymc.Geometric
      • pymc.HyperGeometric
      • pymc.NegativeBinomial
      • pymc.OrderedLogistic
      • pymc.OrderedProbit
      • pymc.Poisson
    • Multivariate
      • pymc.CAR
      • pymc.Dirichlet
      • pymc.DirichletMultinomial
      • pymc.ICAR
      • pymc.KroneckerNormal
      • pymc.LKJCholeskyCov
      • pymc.LKJCorr
      • pymc.MatrixNormal
      • pymc.Multinomial
      • pymc.MvNormal
      • pymc.MvStudentT
      • pymc.OrderedMultinomial
      • pymc.StickBreakingWeights
      • pymc.Wishart
      • pymc.WishartBartlett
      • pymc.ZeroSumNormal
    • Mixture
      • pymc.Mixture
      • pymc.NormalMixture
      • pymc.ZeroInflatedBinomial
      • pymc.ZeroInflatedNegativeBinomial
      • pymc.ZeroInflatedPoisson
      • pymc.HurdlePoisson
      • pymc.HurdleNegativeBinomial
      • pymc.HurdleGamma
      • pymc.HurdleLogNormal
    • Timeseries
      • pymc.AR
      • pymc.EulerMaruyama
      • pymc.GARCH11
      • pymc.GaussianRandomWalk
      • pymc.MvGaussianRandomWalk
      • pymc.MvStudentTRandomWalk
    • Truncated
    • Censored
    • CustomDist
    • Simulator
    • Transformations
      • pymc.distributions.transforms.circular
      • pymc.distributions.transforms.log
      • pymc.distributions.transforms.log_exp_m1
      • pymc.distributions.transforms.logodds
      • pymc.distributions.transforms.ordered
      • pymc.distributions.transforms.simplex
      • pymc.distributions.transforms.CholeskyCovPacked
      • pymc.distributions.transforms.CircularTransform
      • pymc.distributions.transforms.Interval
      • pymc.distributions.transforms.LogExpM1
      • pymc.distributions.transforms.LogOddsTransform
      • pymc.distributions.transforms.LogTransform
      • pymc.distributions.transforms.Ordered
      • pymc.distributions.transforms.SimplexTransform
      • pymc.distributions.transforms.ZeroSumTransform
      • pymc.distributions.transforms.Chain
    • Distribution utilities
      • pymc.Continuous
      • pymc.Discrete
      • pymc.Distribution
      • pymc.SymbolicRandomVariable
      • pymc.DiracDelta
  • Gaussian Processes
    • Implementations
      • pymc.gp.HSGP
      • pymc.gp.HSGPPeriodic
      • pymc.gp.Latent
      • pymc.gp.LatentKron
      • pymc.gp.Marginal
      • pymc.gp.MarginalKron
      • pymc.gp.MarginalApprox
      • pymc.gp.TP
    • Mean Functions
      • pymc.gp.mean.Zero
      • pymc.gp.mean.Constant
      • pymc.gp.mean.Linear
    • Covariance Functions
      • pymc.gp.cov.Constant
      • pymc.gp.cov.WhiteNoise
      • pymc.gp.cov.ExpQuad
      • pymc.gp.cov.RatQuad
      • pymc.gp.cov.Exponential
      • pymc.gp.cov.Matern52
      • pymc.gp.cov.Matern32
      • pymc.gp.cov.Linear
      • pymc.gp.cov.Polynomial
      • pymc.gp.cov.Cosine
      • pymc.gp.cov.Periodic
      • pymc.gp.cov.WarpedInput
      • pymc.gp.cov.Gibbs
      • pymc.gp.cov.Coregion
      • pymc.gp.cov.ScaledCov
      • pymc.gp.cov.Kron
    • GP Utilities
      • pymc.gp.util.plot_gp_dist
  • Model
    • Model creation and inspection
      • pymc.model.core.Model
      • pymc.model.core.modelcontext
    • Model Conditioning
      • pymc.model.transform.do
      • pymc.model.transform.observe
      • pymc.model.transform.change_value_transforms
      • pymc.model.transform.remove_value_transforms
    • Value Transforms
      • pymc.model.transform_values.constrain_values
      • pymc.model.transform_values.unconstrain_values
    • Deterministic Surgery
      • pymc.model.transform.extract_deterministics
      • pymc.model.transform.insert_deterministics
    • Model Optimization
      • pymc.model.transform.freeze_dims_and_data
    • FunctionGraph
      • pymc.model.fgraph.clone_model
      • pymc.model.fgraph.fgraph_from_model
      • pymc.model.fgraph.model_from_fgraph
  • Samplers
    • pymc.sample
    • pymc.sample_prior_predictive
    • pymc.sample_posterior_predictive
    • pymc.draw
    • pymc.compute_deterministics
    • pymc.vectorize_over_posterior
    • pymc.init_nuts
    • pymc.sampling.jax.sample_blackjax_nuts
    • pymc.sampling.jax.sample_numpyro_nuts
    • pymc.step_methods.hmc.NUTS
    • pymc.step_methods.hmc.HamiltonianMC
    • pymc.step_methods.BinaryGibbsMetropolis
    • pymc.step_methods.BinaryMetropolis
    • pymc.step_methods.CategoricalGibbsMetropolis
    • pymc.step_methods.CauchyProposal
    • pymc.step_methods.DEMetropolis
    • pymc.step_methods.DEMetropolisZ
    • pymc.step_methods.LaplaceProposal
    • pymc.step_methods.Metropolis
    • pymc.step_methods.MultivariateNormalProposal
    • pymc.step_methods.NormalProposal
    • pymc.step_methods.PoissonProposal
    • pymc.step_methods.UniformProposal
    • pymc.step_methods.CompoundStep
    • pymc.step_methods.Slice
  • Variational Inference
    • pymc.ADVI
    • pymc.ASVGD
    • pymc.SVGD
    • pymc.FullRankADVI
    • pymc.fit
    • pymc.variational.ImplicitGradient
    • pymc.variational.Inference
    • pymc.variational.KLqp
    • pymc.Empirical
    • pymc.FullRank
    • pymc.MeanField
    • pymc.sample_approx
    • pymc.Group
    • pymc.variational.Approximation
    • pymc.variational.operators.KL
    • pymc.variational.operators.KSD
    • pymc.variational.Stein
    • pymc.adadelta
    • pymc.adagrad
    • pymc.adagrad_window
    • pymc.adam
    • pymc.adamax
    • pymc.apply_momentum
    • pymc.apply_nesterov_momentum
    • pymc.momentum
    • pymc.nesterov_momentum
    • pymc.norm_constraint
    • pymc.rmsprop
    • pymc.sgd
    • pymc.total_norm_constraint
  • Sequential Monte Carlo
    • pymc.smc.sample_smc
    • pymc.smc.kernels.SMC_KERNEL
    • pymc.smc.kernels.IMH
    • pymc.smc.kernels.MH
  • Data
    • pymc.Data
    • pymc.get_data
    • pymc.Minibatch
  • Ordinary differential equations (ODEs)
    • pymc.ode.DifferentialEquation
  • Probability
    • pymc.logp
    • pymc.logcdf
    • pymc.icdf
    • pymc.logprob.conditional_logp
    • pymc.logprob.transformed_conditional_logp
  • Stats
    • pymc.stats.compute_log_prior
    • pymc.stats.compute_log_likelihood
  • Tuning
    • pymc.find_hessian
    • pymc.find_MAP
  • Math
  • PyTensor utils
    • pymc.pytensorf.compile
    • pymc.pytensorf.gradient
    • pymc.pytensorf.hessian
    • pymc.pytensorf.hessian_diag
    • pymc.pytensorf.jacobian
    • pymc.pytensorf.inputvars
    • pymc.pytensorf.cont_inputs
    • pymc.pytensorf.floatX
    • pymc.pytensorf.intX
    • pymc.pytensorf.constant_fold
    • pymc.pytensorf.CallableTensor
    • pymc.pytensorf.join_nonshared_inputs
    • pymc.pytensorf.make_shared_replacements
    • pymc.pytensorf.convert_data
  • shape_utils
    • pymc.distributions.shape_utils.to_tuple
    • pymc.distributions.shape_utils.rv_size_is_none
    • pymc.distributions.shape_utils.change_dist_size
  • Storage backends
    • pymc.to_inference_data
    • pymc.predictions_to_inference_data
    • pymc.backends.NDArray
    • pymc.backends.base.BaseTrace
    • pymc.backends.base.MultiTrace
    • pymc.backends.zarr.ZarrTrace
    • pymc.backends.zarr.ZarrChain
  • Other utils
    • pymc.find_constrained_prior
    • pymc.blocking.DictToArrayBijection
  • Testing
    • pymc.testing.mock_sample
    • pymc.testing.mock_sample_setup_and_teardown
  • Dims
    • Model constructors
      • pymc.dims.Data
      • pymc.dims.Deterministic
      • pymc.dims.Potential
    • Mathematical operations with dimensions
    • Distributions
      • pymc.dims.Flat
      • pymc.dims.HalfFlat
      • pymc.dims.Normal
      • pymc.dims.HalfNormal
      • pymc.dims.TruncatedNormal
      • pymc.dims.LogNormal
      • pymc.dims.StudentT
      • pymc.dims.HalfStudentT
      • pymc.dims.Cauchy
      • pymc.dims.HalfCauchy
      • pymc.dims.Beta
      • pymc.dims.Laplace
      • pymc.dims.Gamma
      • pymc.dims.InverseGamma
      • pymc.dims.Weibull
      • pymc.dims.Categorical
      • pymc.dims.MvNormal
      • pymc.dims.ZeroSumNormal
      • pymc.dims.Censored
    • Distribution Transforms
      • pymc.dims.transforms.LogTransform
      • pymc.dims.transforms.LogOddsTransform
      • pymc.dims.transforms.ZeroSumTransform
  • API
  • Distributions
  • Distribution utilities
  • pymc.SymbolicRandomVariable
  • pymc.SymbolicRandomVariable.link_fgraph

pymc.SymbolicRandomVariable.link_fgraph#

SymbolicRandomVariable.link_fgraph(fgraph, mode)#

Link an already-baked inner fgraph under mode, no rewrites.

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pymc.SymbolicRandomVariable.inplace_on_inputs

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pymc.SymbolicRandomVariable.link_mode
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  • SymbolicRandomVariable.link_fgraph()
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