Abstract
All the MATLAB code examples accompanying this book can be run directly. The examples are self-contained and do not require additional path variables being set up. The following is a partial list of the supplementary MATLAB functions that are called at various stages by the state estimators.
You have full access to this open access chapter, Download chapter PDF
All the MATLAB code examples accompanying this book can be run directly. The examples are self-contained and do not require additional path variables being set up. The following is a partial list of the supplementary MATLAB functions that are called at various stages by the state estimators.
-
get_linear_parameters (\(\ldots \))
Calculates the updates for the constant coefficients (e.g., \(\gamma _{0}\) and \(\gamma _{1}\)) for a continuous variable (e.g., \(r_{k}\)). If this function is present in a MATLAB example where there is an MPP, but not a continuous variable, then it calculates the constant coefficients based on the MPP amplitudes.
-
get_maximum_variance (\(\ldots \)) or get_continuous_variable_variance_update (\(\ldots \))
Calculates the sensor noise variance update (e.g., \(\sigma ^{2}_{v}\)) for a continuous variable (e.g., \(r_{k}\)). If this function is present in a MATLAB example where there is an MPP, but not a continuous variable, then it calculates the sensor noise variance based on the MPP amplitudes.
-
get_linear_parameters_for_mpp (\(\ldots \))
Calculates the updates for the constant coefficients (e.g., \(\gamma _{0}\) and \(\gamma _{1}\)) for a series of MPP amplitudes (e.g., \(r_{k}\)). This function is used to calculate the updates corresponding to an MPP when a continuous variable is also present.
-
get_maximum_variance_for_mpp (\(\ldots \))
Calculates the sensor noise variance update (e.g., \(\sigma ^{2}_{v}\)) for a series of MPP amplitudes. This function is used to calculate the update corresponding to an MPP when a continuous variable is also present.
-
get_posterior_mode (\(\ldots \)) or get_state_update (\(\ldots \))
Calculates the update \(x_{k|k}\) based on the Newton–Raphson method
-
get_pk_conf_lims (\(\ldots \))
Calculates the confidence limits for the probability of binary event occurrence \(p_{k}\)
-
get_certainty_curve (\(\ldots \))
Calculates the HAI value based on the probability of binary event occurrence \(p_{k}\) exceeding a baseline value
-
rhythm (\(\ldots \))
Calculates the cortisol-related circadian term \(I_{k}\) in the state equation
-
circadian_parameters (\(\ldots \))
Calculates the log-likelihood term to be optimized when estimating the (cortisol-related) circadian rhythm terms in the state equation
-
get_log_likelihood (\(\ldots \))
Calculates the log-likelihood of the term involving the CIF
-
get_ks_plot(\(\ldots \))
Calculates the Kolmogorov–Smirnov (KS) plot for assessing the goodness of fit of a CIF to point process observations
-
Other functions related to a CIF
Functions such as fetch_lambda (\(\ldots \)), dlambda_dx (\(\ldots \)), f (\(\ldots \)), and mu (\(\ldots \)) are all supplementary functions that calculate various components or derivatives related to an HDIG-based CIF
Author information
Authors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2024 The Author(s)
About this chapter
Cite this chapter
Wickramasuriya, D.S., Faghih, R.T. (2024). List of Supplementary MATLAB Functions. In: Bayesian Filter Design for Computational Medicine. Springer, Cham. https://doi.org/10.1007/978-3-031-47104-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-47104-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47103-2
Online ISBN: 978-3-031-47104-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)