get_predictive_dist_KF_objective_prior: Updating the predictive distribution
In SKFCPD: Fast Online Changepoint Detection for Temporally Correlated Data

get_predictive_dist_KF_objective_prior

R Documentation

Updating the predictive distribution

Description

Updating the predictive distribution of the run length under the objective prior.

Usage

  get_predictive_dist_KF_objective_prior(cur_input, cur_num_obs,
  params, prev_L, d, gamma, model_type, mu, sigma_2, eta, kernel_type)

Arguments

`cur_input`	A value of current observation.
`cur_num_obs`	A value of index for the current observation.
`params`	A list of current Kalman filter parameters.
`prev_L`	A list of previous Kalman filter parameters.
`d`	A value of the distance between the sorted input.
`gamma`	A numeric variable of the range parameter for the covariance matrix.
`model_type`	A numeric variable that can take values of 0, 1 and 2. Model_type=0 stands for a GP model with unknown mean and known variance. Model_type=1 stands for a GP model with known mean and unknown variance. Model_type=2 stands for a GP model with unknown mean and unknown variance.
`mu`	A vector of the mean parameter at each coordinate. Ignored when model_type = 0 or 2.
`sigma_2`	A vector of the variance parameter at each coordinate.
`eta`	A vector of the noise-to-signal ratio at each coordinate
`kernel_type`	A character specifying the type of kernels of the input. `matern_5_2` are `Matern correlation` with roughness parameter 5/2. `exp` is power exponential correlation with roughness parameter alpha=2.

Value

get_predictive_dist_KF_objective_prior returns a list of updated predictive distribution of the run length under the objective prior.

Author(s)

Hanmo Li [aut, cre], Yuedong Wang [aut], Mengyang Gu [aut]

Maintainer: Hanmo Li <hanmo@pstat.ucsb.edu>

References

Fearnhead, P., & Liu, Z. (2007). On-line inference for multiple changepoint problem. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(4), 589-605.

Adams, R. P., & MacKay, D. J. (2007). Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742.

Hartikainen, J. and Sarkka, S. (2010). Kalman filtering and smoothing solutions to temporal gaussian process regression models, Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop, 379-384.

SKFCPD documentation built on June 22, 2024, 11:06 a.m.