pypomp.models.LG

pypomp.models.LG(T: int = 4, A: ~jax.Array = Array([[ 0.9800666 , -0.19866933],        [ 0.19866933,  0.9800666 ]], dtype=float32), C: ~jax.Array = Array([[1., 0.],        [0., 1.]], dtype=float32), Q: ~jax.Array = Array([[0.01  , 0.0002],        [0.0002, 0.01  ]], dtype=float32), R: ~jax.Array = Array([[0.1 , 0.01],        [0.01, 0.1 ]], dtype=float32), key: ~jax.Array = Array((), dtype=key<fry>) overlaying: [0 1]) → Pomp[source]

Initialize a Pomp object with the linear Gaussian model.

Parameters:

  • T (int, optional) – The number of time steps to generate data for. Defaults to 4.

  • A (jax.Array, optional) – The transition matrix.

  • C (jax.Array, optional) – The measurement matrix.

  • Q (jax.Array, optional) – The covariance matrix of the state noise.

  • R (jax.Array, optional) – The covariance matrix of the measurement noise.

  • key (jax.Array, optional) – The random key used to generate the data.

Return type:

A Pomp object initialized with the linear Gaussian model parameters and the generated data.