pgmuvi.gps

class pgmuvi.gps.SparseSpectralMixtureGPModel(*args: Any, **kwargs: Any)

Bases: ApproximateGP

A one-dimensional GP model using a spectral mixture kernel

A longer description goes here

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

Examples

Notes

forward(x)
class pgmuvi.gps.SpectralMixtureGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A one-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is constant.

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

Examples

Notes

forward(x)
class pgmuvi.gps.SpectralMixtureKISSGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A one-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is constant. It uses the Kernel interpolation for scalable structured Gaussian processes (KISS-GP) approximation to enable scaling to much larger datasets. This means it becomes effective when your dataset exceeds ~10,000 entries; for smaller datasets, the overhead of interpolation is typically not worth the effort.

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

  • grid_size (int) – The number of points to use in the kernel interpolation grid.

Examples

Notes

forward(x)
class pgmuvi.gps.SpectralMixtureLinearMeanGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A one-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is a linear function.

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

Examples

Notes

forward(x)
class pgmuvi.gps.SpectralMixtureLinearMeanKISSGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A one-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is a linear function. It uses the Kernel interpolation for scalable structured Gaussian processes (KISS-GP) approximation to enable scaling to much larger datasets. This means it becomes effective when your dataset exceeds ~10,000 entries; for smaller datasets, the overhead of interpolation is typically not worth the effort.

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

  • grid_size (int) – The number of points to use in the kernel interpolation grid.

Examples

Notes

forward(x)
class pgmuvi.gps.TwoDSpectralMixtureGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A two-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is constant. It supports datasets with two independent variables (e.g. time and wavelength).

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps and wavelengths)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

Examples

Notes

forward(x)
class pgmuvi.gps.TwoDSpectralMixtureKISSGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A two-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is constant. It supports datasets with two independent variables (e.g. time and wavelength). It uses the Kernel interpolation for scalable structured Gaussian processes (KISS-GP) approximation to enable scaling to much larger datasets. This means it becomes effective when your dataset exceeds ~10,000 entries; for smaller datasets, the overhead of interpolation is typically not worth the effort.

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

  • grid_size ((2x1) iterable of ints) – The number of points to use in the kernel interpolation grid, with one value per dimension.

Examples

Notes

forward(x)
class pgmuvi.gps.TwoDSpectralMixtureLinearMeanGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A two-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is a linear function. It supports datasets with two independent variables (e.g. time and wavelength).

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps and wavelengths)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

Examples

Notes

forward(x)
class pgmuvi.gps.TwoDSpectralMixtureLinearMeanKISSGPModel(*args: Any, **kwargs: Any)

Bases: ExactGP

A two-dimensional GP model using a spectral mixture kernel

A Gaussian Process which uses a Spectral Mixture Kernel to model the Power Spectral Density of the covariance matrix as a Gaussian Mixture Model. This model assumes the mean is a linear function. It supports datasets with two independent variables (e.g. time and wavelength). It uses the Kernel interpolation for scalable structured Gaussian processes (KISS-GP) approximation to enable scaling to much larger datasets. This means it becomes effective when your dataset exceeds ~10,000 entries; for smaller datasets, the overhead of interpolation is typically not worth the effort.

Parameters:

  • train_x (Tensor) – The data for the independent variable (typically timestamps)

  • train_y (Tensor) – The data for the dependent variable (typically flux)

  • likelihood (a Likelihood object or subclass) – The likelihood that will be used to evaluate the model

  • num_mixtures (int) – Number of components in the Mixture Model. More mixtures gives more flexibility, but more hyperparameters and more complex inference

  • grid_size ((2x1) iterable of ints) – The number of points to use in the kernel interpolation grid, with one value per dimension.

Examples

Notes

forward(x)