You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_spm_vb.m
    path of file: ./repos/malini/Codes/deconvolution - model/spm8
    
    the code of the file until where you have to start completion:   function [SPM] = spm_spm_vb(SPM)
% VB estimation of GLM-AR models with spatial regularisation
% FORMAT [SPM] = spm_spm_vb(SPM)
%
% This function implements a VB estimation scheme for GLM-AR models. 
% Both regression coefficients and AR coefficients are spatially
% regularised. The algorithm is described in a series of papers:
%
% Paper VB1: W. Penny, S. Kiebel and K. Friston (2003).
%            Variational Bayesian inference for fMRI time series. 
%            NeuroImage 19(3), pp 727-741.
%
% Paper VB2: W. Penny, N. Trujillo-Barreto and K. Friston (2005).
%            Bayesian fMRI time series analysis with spatial priors.
%            NeuroImage 24(2), pp 350-362.
%
% Paper VB3: W. Penny and G. Flandin (2005).
%            Bayesian analysis of single-subject fMRI: SPM implementation.
%            Technical Report. WDIN, UCL.
%
% Paper VB4: W. Penny, G. Flandin and N. Trujillo-Barreto (2007).
%            Bayesian comparison of spatially regularised general linear
%            models. Human Brain Mapping 28(4):275-293.
%
% Paper VB5: W. Penny and G. Flandin (2005).
%            Bayesian analysis of fMRI data with spatial priors.
%            2005 Proceedings of the Joint Statistical Meeting, Section on
%            Statistical Graphics [CDROM], Alexandria, VA: American 
%            Statistical Association.  
%
% The space to be analysed is a 'Volume', 'Slices' or 'Clusters'.
% For 'Slices' the numbers of the slices to be analysed are then entered.
% For 'Clusters' a mask specifying the volume to be analysed is entered.
% ______________________________________________________________________ 
%
% Required fields of SPM:
%
% xY.VY - nScan x 1 struct array of mapped image volumes
%         Images must have the same orientation, voxel size and data type
%       - Any scaling should have already been applied via the image
%         handle scalefactors.
%
% xX    - Structure containing design matrix information
%       - Required fields are:
%         xX.X      - Design matrix (raw, not temporally smoothed)
%         xX.name   - cellstr of parameter names corresponding to 
%                     columns of design matrix
%
% xM    - Structure containing masking information, or a simple column
%         vector of thresholds corresponding to the images in VY.
%       - If a structure, the required fields are:
%         xM.TH - nVar x nScan matrix of analysis thresholds, one per image
%         xM.I  - Implicit masking (0=>none, 1 => implicit zero/NaN mask)
%         xM.VM - struct array of mapped explicit mask image volumes
%       - (empty if no explicit masks)
%               - Explicit mask images are >0 for valid voxels to assess.
%               - Mask images can have any orientation, voxel size or 
%                 data type. They are interpolated using nearest neighbour
%                 interpolation to the voxel locations of the data Y.
%       - Note that voxels with constant data (i.e. the same value 
%         across scans) are also automatically masked out.
%         
% ______________________________________________________________________
%
% spm_spm_vb adds the following fields to SPM:
%
%   SPM.VCbeta - Handles of posterior parameter estimates 
%                (Cbeta_????)
%   SPM.VPsd   - Handles of SD of posterior parameter estimates
%                (SDbeta_????)
%
%   SPM.PPM    - Posterior Probability Map data structure
%
%                .VB=1, tells later functions (spm_contrasts, spm_graph)
%                       that parameters were estimated using V
    
function [SPM] = spm_spm_vb(SPM)
% VB estimation of GLM-AR models with spatial regularisation
% FORMAT [SPM] = spm_spm_vb(SPM)
%
% This function implements a VB estimation scheme for GLM-AR models. 
% Both regression coefficients and AR coefficients are spatially
% regularised. The algorithm is described in a series of papers:
%
% Paper VB1: W. Penny, S. Kiebel and K. Friston (2003).
%            Variational Bayesian inference for fMRI time series. 
%            NeuroImage 19(3), pp 727-741.
%
% Paper VB2: W. Penny, N. Trujillo-Barreto and K. Friston (2005).
%            Bayesian fMRI time series analysis with spatial priors.
%            NeuroImage 24(2), pp 350-362.
%
% Paper VB3: W. Penny and G. Flandin (2005).
%            Bayesian analysis of single-subject fMRI: SPM implementation.
%            Technical Report. WDIN, UCL.
%
% Paper VB4: W. Penny, G. Flandin and N. Trujillo-Barreto (2007).
%            Bayesian comparison of spatially regularised general linear
%            models. Human Brain Mapping 28(4):275-293.
%
% Paper VB5: W. Penny and G. Flandin (2005).
%            Bayesian analysis of fMRI data with spatial priors.
%            2005 Proceedings of the Joint Statistical Meeting, Section on
%            Statistical Graphics [CDROM], Alexandria, VA: American 
%            Statistical Association.  
%
% The space to be analysed is a 'Volume', 'Slices' or 'Clusters'.
% For 'Slices' the numbers of the slices to be analysed are then entered.
% For 'Clusters' a mask specifying the volume to be analysed is entered.
% ______________________________________________________________________ 
%
% Required fields of SPM:
%
% xY.VY - nScan x 1 struct array of mapped image volumes
%         Images must have the same orientation, voxel size and data type
%       - Any scaling should have already been applied via the image
%         handle scalefactors.
%
% xX    - Structure containing design matrix information
%       - Required fields are:
%         xX.X      - Design matrix (raw, not temporally smoothed)
%         xX.name   - cellstr of parameter names corresponding to 
%                     columns of design matrix
%
% xM    - Structure containing masking information, or a simple column
%         vector of thresholds corresponding to the images in VY.
%       - If a structure, the required fields are:
%         xM.TH - nVar x nScan matrix of analysis thresholds, one per image
%         xM.I  - Implicit masking (0=>none, 1 => implicit zero/NaN mask)
%         xM.VM - struct array of mapped explicit mask image volumes
%       - (empty if no explicit masks)
%               - Explicit mask images are >0 for valid voxels to assess.
%               - Mask images can have any orientation, voxel size or 
%                 data type. They are interpolated using nearest neighbour
%                 interpolation to the voxel locations of the data Y.
%       - Note that voxels with constant data (i.e. the same value 
%         across scans) are also automatically masked out.
%         
% ______________________________________________________________________
%
% spm_spm_vb adds the following fields to SPM:
%
%   SPM.VCbeta - Handles of posterior parameter estimates 
%                (Cbeta_????)
%   SPM.VPsd   - Handles of SD of posterior parameter estimates
%                (SDbeta_????)
%
%   SPM.PPM    - Posterior Probability Map data structure
%
%                .VB=1, tells later functions (spm_contrasts, spm_graph)
%                       that parameters were estimated using VB
%                       
%                .AR_P, assumed AR model order
%
%                .priors, type of priors used (eg. 'Spatial-GMRF')
%                       see spm_vb_set_priors.m
%
%                .update_F, whether model evidence is to be computed
%
%                .Gamma, default effect size threshold (used in spm_getSPM)
%
%                   info is stored for each "block", where a block
%                   is either a slice or subvolume, computed using a 
%                   graph-partitioning algorithm. This is stored in
%                       .Sess(s).block(z), further info about GLM-AR 
%                                  model at block z eg. block(z).F is
%                                  evidence for block z (if computed)
%                                  where s is the session number
%                   The following parameters are set if the space to be
%                   analysed chosen as 'Slices'
%                       .AN_slices, numbers of slices analysed
%
% For each session the following fields are also specified:
%
%   SPM.PPM.Sess(s).VHp  - Handle of standard deviation of the error
%                          (Sess%s%_SDerror)
%   SPM.PPM.Sess(s).VAR  - Handles of AR coefficient images
%                          (Sess%s%_AR_????)
%
% If contrasts have been specified prior to estimation (this is 
% recommend