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: vbmc.m
    path of file: ./repos/vbmc
    
    the code of the file until where you have to start completion: function [vp,elbo,elbo_sd,exitflag,output,samples,optimState,stats,vp_train] = ...
    vbmc(fun,x0,LB,UB,PLB,PUB,options,varargin)
%VBMC Posterior and model inference via Variational Bayesian Monte Carlo (v1.0.12).
%   VBMC computes a variational approximation of the full posterior and a 
%   lower bound on the normalization constant (marginal likelhood or model
%   evidence) for a provided unnormalized log posterior. As of v1.0, VBMC
%   also supports noisy evaluations of the log posterior (see below).
%
%   VP = VBMC(FUN,X0,LB,UB) initializes the variational posterior in the
%   proximity of X0 (ideally, a posterior mode) and iteratively computes
%   a variational approximation for a given target log posterior FUN.
%   FUN accepts input X and returns the value of the target log-joint, that
%   is the unnormalized log-posterior density, at X. LB and UB define a set 
%   of strict lower and upper bounds coordinate vector, X, so that the 
%   posterior has support on LB < X < UB. LB and UB can be scalars or 
%   vectors. If scalars, the bound is replicated in each dimension. Use 
%   empty matrices for LB and UB if no bounds exist. Set LB(i) = -Inf and 
%   UB(i) = Inf if the i-th coordinate is unbounded (while other coordinates 

    
function [vp,elbo,elbo_sd,exitflag,output,samples,optimState,stats,vp_train] = ...
    vbmc(fun,x0,LB,UB,PLB,PUB,options,varargin)
%VBMC Posterior and model inference via Variational Bayesian Monte Carlo (v1.0.12).
%   VBMC computes a variational approximation of the full posterior and a 
%   lower bound on the normalization constant (marginal likelhood or model
%   evidence) for a provided unnormalized log posterior. As of v1.0, VBMC
%   also supports noisy evaluations of the log posterior (see below).
%
%   VP = VBMC(FUN,X0,LB,UB) initializes the variational posterior in the
%   proximity of X0 (ideally, a posterior mode) and iteratively computes
%   a variational approximation for a given target log posterior FUN.
%   FUN accepts input X and returns the value of the target log-joint, that
%   is the unnormalized log-posterior density, at X. LB and UB define a set 
%   of strict lower and upper bounds coordinate vector, X, so that the 
%   posterior has support on LB < X < UB. LB and UB can be scalars or 
%   vectors. If scalars, the bound is replicated in each dimension. Use 
%   empty matrices for LB and UB if no bounds exist. Set LB(i) = -Inf and 
%   UB(i) = Inf if the i-th coordinate is unbounded (while other coordinates 
%   may be bounded). Note that if LB and UB contain unbounded variables, 
%   the respective values of PLB and PUB need to be specified (see below). 
%   VBMC returns a variational posterior solution VP, which can then be 
%   manipulated via other functions in the VBMC toolbox (see examples below).
%
%   VP = VBMC(FUN,X0,LB,UB,PLB,PUB) specifies a set of plausible lower and
%   upper bounds such that LB < PLB < PUB < UB. Both PLB and PUB
%   need to be finite. PLB and PUB represent a "plausible" range, which
%   should denote a region of high posterior probability mass. Among other 
%   things, the plausible box is used to draw initial samples and to set 
%   priors over hyperparameters of the algorithm. When in doubt, we found 
%   that setting PLB and PUB using the topmost ~68% percentile range of the 
%   prior (e.g, mean +/- 1 SD for a Gaussian prior) works well in many 
%   cases (but note that additional information might afford a better guess).
%  
%   VP = VBMC(FUN,X0,LB,UB,PLB,PUB,OPTIONS) performs variational inference
%   with the default parameters replaced by values in the structure OPTIONS.
%   VBMC('defaults') returns the default OPTIONS struct.
%  
%   VP = VBMC(FUN,X0,LB,UB,PLB,PUB,OPTIONS,...) passes additional arguments
%   to FUN.
%
%   VBMC also supports noisy/stochastic estimates of the log-posterior,
%   obtained through techniques such as Inverse Binomial Sampling (see 
%   examples and references below). For noisy evaluations, FUN should 
%   return as second agument the estimated SD (standard deviation) of the 
%   log-likelihood noise at X. 
%   Set OPTIONS.SpecifyTargetNoise = 1 to activate support for noisy
%   inference (this is not automatic).
%  
%   VP = VBMC(FUN,VP0,...) uses variational posterior VP0 (from a previous
%   run of VBMC) to initialize the current run. You can leave PLB and PUB
%   empty, in which case they will be set using VP0 (recommend