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  • Distributions
    • Continuous
      • pymc.Uniform
      • pymc.Flat
      • pymc.HalfFlat
      • pymc.Normal
      • pymc.TruncatedNormal
      • pymc.Beta
      • pymc.Kumaraswamy
      • pymc.Exponential
      • pymc.Laplace
      • pymc.StudentT
      • pymc.Cauchy
      • pymc.HalfCauchy
      • pymc.Gamma
      • pymc.Weibull
      • pymc.HalfStudentT
      • pymc.LogNormal
      • pymc.ChiSquared
      • pymc.HalfNormal
      • pymc.Wald
      • pymc.Pareto
      • pymc.InverseGamma
      • pymc.ExGaussian
      • pymc.VonMises
      • pymc.SkewNormal
      • pymc.Triangular
      • pymc.Gumbel
      • pymc.Logistic
      • pymc.LogitNormal
      • pymc.Interpolated
      • pymc.Rice
      • pymc.Moyal
      • pymc.AsymmetricLaplace
      • pymc.PolyaGamma
    • Discrete
      • pymc.Binomial
      • pymc.BetaBinomial
      • pymc.Bernoulli
      • pymc.DiscreteWeibull
      • pymc.Poisson
      • pymc.NegativeBinomial
      • pymc.DiracDelta
      • pymc.ZeroInflatedPoisson
      • pymc.ZeroInflatedBinomial
      • pymc.ZeroInflatedNegativeBinomial
      • pymc.DiscreteUniform
      • pymc.Geometric
      • pymc.HyperGeometric
      • pymc.Categorical
      • pymc.OrderedLogistic
      • pymc.OrderedProbit
    • Multivariate
      • pymc.MvNormal
      • pymc.MvStudentT
      • pymc.ZeroSumNormal
      • pymc.Dirichlet
      • pymc.Multinomial
      • pymc.DirichletMultinomial
      • pymc.OrderedMultinomial
      • pymc.Wishart
      • pymc.WishartBartlett
      • pymc.LKJCorr
      • pymc.LKJCholeskyCov
      • pymc.MatrixNormal
      • pymc.KroneckerNormal
      • pymc.CAR
      • pymc.StickBreakingWeights
    • Mixture
      • pymc.Mixture
      • pymc.NormalMixture
    • Timeseries
      • pymc.AR
      • pymc.GaussianRandomWalk
      • pymc.GARCH11
      • pymc.EulerMaruyama
      • pymc.MvGaussianRandomWalk
      • pymc.MvStudentTRandomWalk
    • Truncated
      • pymc.Truncated
    • Censored
      • pymc.Censored
    • Simulator
      • pymc.Simulator
    • Transformations
      • pymc.distributions.transforms.simplex
      • pymc.distributions.transforms.logodds
      • pymc.distributions.transforms.log_exp_m1
      • pymc.distributions.transforms.log
      • pymc.distributions.transforms.sum_to_1
      • pymc.distributions.transforms.circular
      • pymc.distributions.transforms.CholeskyCovPacked
      • pymc.distributions.transforms.Interval
      • pymc.distributions.transforms.LogExpM1
      • pymc.distributions.transforms.Ordered
      • pymc.distributions.transforms.SumTo1
      • pymc.distributions.transforms.ZeroSumTransform
      • pymc.distributions.transforms.Chain
    • Distribution utilities
      • pymc.Distribution
      • pymc.Discrete
      • pymc.Continuous
      • pymc.CustomDist
      • pymc.SymbolicRandomVariable
  • Gaussian Processes
    • Implementations
      • 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
  • Model
  • Samplers
    • pymc.sample
    • pymc.sample_prior_predictive
    • pymc.sample_posterior_predictive
    • pymc.sample_posterior_predictive_w
    • pymc.sampling.jax.sample_blackjax_nuts
    • pymc.sampling.jax.sample_numpyro_nuts
    • pymc.init_nuts
    • pymc.draw
    • pymc.NUTS
    • pymc.HamiltonianMC
    • pymc.BinaryGibbsMetropolis
    • pymc.BinaryMetropolis
    • pymc.CategoricalGibbsMetropolis
    • pymc.CauchyProposal
    • pymc.DEMetropolis
    • pymc.DEMetropolisZ
    • pymc.LaplaceProposal
    • pymc.Metropolis
    • pymc.MultivariateNormalProposal
    • pymc.NormalProposal
    • pymc.PoissonProposal
    • pymc.UniformProposal
    • pymc.CompoundStep
    • pymc.Slice
  • Variational Inference
    • pymc.ADVI
    • pymc.ASVGD
    • pymc.SVGD
    • pymc.FullRankADVI
    • pymc.ImplicitGradient
    • pymc.Inference
    • pymc.KLqp
    • pymc.fit
    • pymc.Empirical
    • pymc.FullRank
    • pymc.MeanField
    • pymc.sample_approx
    • pymc.Approximation
    • pymc.Group
    • pymc.variational.operators.KL
    • pymc.variational.operators.KSD
    • pymc.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
  • Storage backends
    • pymc.to_inference_data
    • pymc.predictions_to_inference_data
    • pymc.backends.NDArray
    • pymc.backends.base.BaseTrace
    • pymc.backends.base.MultiTrace
  • Data
    • pymc.ConstantData
    • pymc.MutableData
    • pymc.get_data
    • pymc.Data
    • pymc.GeneratorAdapter
    • pymc.Minibatch
  • Ordinary differential equations (ODEs)
    • pymc.ode.DifferentialEquation
  • Tuning
    • pymc.find_hessian
    • pymc.guess_scaling
    • pymc.trace_cov
    • pymc.find_MAP
  • Math
    • pymc.expand_packed_triangular
    • pymc.logit
    • pymc.invlogit
    • pymc.probit
    • pymc.invprobit
    • pymc.logsumexp
    • pymc.math.dot
    • pymc.math.constant
    • pymc.math.flatten
    • pymc.math.zeros_like
    • pymc.math.ones_like
    • pymc.math.stack
    • pymc.math.concatenate
    • pymc.math.sum
    • pymc.math.prod
    • pymc.math.lt
    • pymc.math.gt
    • pymc.math.le
    • pymc.math.ge
    • pymc.math.eq
    • pymc.math.neq
    • pymc.math.switch
    • pymc.math.clip
    • pymc.math.where
    • pymc.math.and_
    • pymc.math.or_
    • pymc.math.abs
    • pymc.math.exp
    • pymc.math.log
    • pymc.math.cos
    • pymc.math.sin
    • pymc.math.tan
    • pymc.math.cosh
    • pymc.math.sinh
    • pymc.math.tanh
    • pymc.math.sqr
    • pymc.math.sqrt
    • pymc.math.erf
    • pymc.math.erfinv
    • pymc.math.dot
    • pymc.math.maximum
    • pymc.math.minimum
    • pymc.math.sgn
    • pymc.math.ceil
    • pymc.math.floor
    • pymc.math.det
    • pymc.math.matrix_inverse
    • pymc.math.extract_diag
    • pymc.math.matrix_dot
    • pymc.math.trace
    • pymc.math.sigmoid
    • pymc.math.logsumexp
    • pymc.math.invlogit
    • pymc.math.logit
  • PyTensor utils
    • pymc.compile_pymc
    • pymc.gradient
    • pymc.hessian
    • pymc.hessian_diag
    • pymc.jacobian
    • pymc.inputvars
    • pymc.cont_inputs
    • pymc.floatX
    • pymc.intX
    • pymc.smartfloatX
    • pymc.constant_fold
    • pymc.CallableTensor
    • pymc.join_nonshared_inputs
    • pymc.make_shared_replacements
    • pymc.generator
    • pymc.convert_observed_data
  • shape_utils
    • pymc.distributions.shape_utils.to_tuple
    • pymc.distributions.shape_utils.shapes_broadcasting
    • pymc.distributions.shape_utils.broadcast_dist_samples_shape
    • pymc.distributions.shape_utils.get_broadcastable_dist_samples
    • pymc.distributions.shape_utils.broadcast_distribution_samples
    • pymc.distributions.shape_utils.broadcast_dist_samples_to
    • pymc.distributions.shape_utils.rv_size_is_none
    • pymc.distributions.shape_utils.change_dist_size
  • Other utils
    • pymc.compute_log_likelihood
    • pymc.find_constrained_prior
    • pymc.DictToArrayBijection
    • pymc.str_for_dist
    • pymc.str_for_model
    • pymc.str_for_potential_or_deterministic

pymc.gp.cov.Matern52#

class pymc.gp.cov.Matern52(input_dim, ls=None, ls_inv=None, active_dims=None)[source]#

The Matern kernel with nu = 5/2.

\[k(x, x') = \left(1 + \frac{\sqrt{5(x - x')^2}}{\ell} + \frac{5(x-x')^2}{3\ell^2}\right) \mathrm{exp}\left[ - \frac{\sqrt{5(x - x')^2}}{\ell} \right]\]

Methods

Matern52.__init__(input_dim[, ls, ls_inv, ...])

Matern52.diag(X)

Matern52.euclidean_dist(X, Xs)

Matern52.full(X[, Xs])

Matern52.square_dist(X, Xs)

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pymc.gp.cov.Exponential.square_dist

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pymc.gp.cov.Matern52.__init__

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