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: fdet_mc3.m
    path of file: ./repos/malini/Codes/FC_EC_calculation code/DynamicFC/jplv7/spatial/lndet_funcs
    
    the code of the file until where you have to start completion: function [confide, moms]=fdet_mc3(d, total_order, iter, oxact, draw_tolerance, asize, randstate, alphafine)
%
%[confide, moms]=fdet_mc3(d, total_order, iter, oxact, draw_tolerance, asize, randstate, alphafine)
%
%This function estimates the Log-determinant of (I-a*d) and provides confidence bounds on the estimated log-determinant
%(a is a scalar, and d is an n by n weight matrix). While the estimated log-determinant is valid for a in (-1/min(eig(d)),1), the 
%lower bound computation requires a in (-1,1).
%
%
%INPUT:
%
%The required n by n matrix d can be a symmetric or asymmetric weight matrix. However, maximum modulus of the eigenvalue of d must be 1 or less. 
%A row-stochastic, doubly stochastic scaling will do this. Matrices similar to stochastic matrices will satisfy this as well.
%Finally, one can find the maximum eigenvalue, and scale a candidate weight matrix by this to yield a weight
%maxtrix with maximum eigenvalue of 1. Note, one can find the maximum eigenvalue of a sparse d using the command 

    
function [confide, moms]=fdet_mc3(d, total_order, iter, oxact, draw_tolerance, asize, randstate, alphafine)
%
%[confide, moms]=fdet_mc3(d, total_order, iter, oxact, draw_tolerance, asize, randstate, alphafine)
%
%This function estimates the Log-determinant of (I-a*d) and provides confidence bounds on the estimated log-determinant
%(a is a scalar, and d is an n by n weight matrix). While the estimated log-determinant is valid for a in (-1/min(eig(d)),1), the 
%lower bound computation requires a in (-1,1).
%
%
%INPUT:
%
%The required n by n matrix d can be a symmetric or asymmetric weight matrix. However, maximum modulus of the eigenvalue of d must be 1 or less. 
%A row-stochastic, doubly stochastic scaling will do this. Matrices similar to stochastic matrices will satisfy this as well.
%Finally, one can find the maximum eigenvalue, and scale a candidate weight matrix by this to yield a weight
%maxtrix with maximum eigenvalue of 1. Note, one can find the maximum eigenvalue of a sparse d using the command 
%eigs in a reasonable time for moderately large matrices. 
%
%The optional scalar integer total_order specifies the highest term estimated in the Taylor series (e.g., E(tr(D^total_order)) ). 
%Please specify even order. The default equals 50.
%
%The optional scalar integer iter specifies how many independ