Structured PCA Model

`X`

The matrix of data

`n`

The number of samples

`k`

Latent dimensionality

`d`

Simensionality of observations

`beta`

Covariance function hyperparameters

`K`

covariance matrix generated from the covariance function and hyperparameters

`KD`

Partial derivatived of K w.r.t each of the hyperparameters

`locs`

Locations (t_i)

`covFn`

Covariance function

`covFnD`

Function giving partial derivatives of covariance function w.r.t hyperparameters

`muHat`

MAP estimate of mu

`WHat`

MAP estimate of W

`sigSqHat`

MAP estimate of sigSq

`Vmean`

The posterior mean of each latent variable

`VVar`

The posterior variance around each latent variable

`logEvidence`

log of the approximate evidence

`logPosteriors`

sequence of posteriors obtained in fitting. Un-normalised if sparse

`logEvidenceD`

Partial derivatives of evidence w.r.t hyperparameters

`H`

The precision matrix for the gaussian approximation to the posterior around each w

`maxim`

the maximisation object returned in hyperparameter tuning

`thetaConv`

Whether theta has been optimised to convergence (if false, beta has probably converged, not theta)

`theta`

betaHist history of betas in optimisation

`convergence`

History of logEvidence and logPosteror; used to assess convergence

`sparse`

whether this is a sparse stpca model

`b`

The sparsity hyperparameter in a sparse model.

`crossvalidate(nFolds = 3, nThreads = 1)`

Perform k-fold cross-validation (possibly multithreaded) to determine the held out log likelihood of examples. This could be used as an alternative to computing the approximate evidence in model selection.

`decode(Vnew)`

Decode points in the latent space into observations

`encode(Xnew)`

Encode a matrix of observations into latent distributions.

`set_b(bNew)`

Setter for 'b'

`set_beta(betaNew)`

Sets beta to a new value, recomputes K, KD and H.

`set_locs(locsNew)`

Sets the locations, and recomputes K. Be careful with this method. Does not update theta

`set_sparse(spNew, b)`

Setter for 'sparse'

`set_X(Xnew)`

Sets X, updates mu. Does not opdate anything else so it is recommended to call update() or something. Be careful with this method.

`simulate(n = 1, Wknown = TRUE)`

Simulates new synthetic data from a fitted model.

`update(tune.maxit = 10, tune.tol = 1e-05, EM.maxit = 500, EM.bftol = 1e-05, ...)`

The major method for performing inference. This iterates between updating theta (using update_theta) and updating beta (using update_beta).

`update_beta(...)`

Optimises the approximate evidence with respect to the hyperparameters.

`update_theta(maxit = 500, bftol = 1e-05)`

Finds the MAP theta using EM.

JimSkinner/spca documentation built on Aug. 19, 2018, 7 a.m.

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