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: distribution.m
    path of file: ./repos/OPEN_FPE_IFT/.previousVersions/V4 Functions
    
    the code of the file until where you have to start completion: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Arguments IN
% tau1 = first increments in steps
% tau2 = second increments in steps
% incr1 = Velocity increment for step tau1
% incr2 = Velocity increment for step tau2
% num_bin = number of bins
% dx = bin width for incr1
% dy = bin width for incr2
% x = Centers of bins for incr1
% y = Centers of bins for incr2
% dA = dx*dy = area of a single 2D bin this will be used for converting probability to probability 
%              density function
% varargin = Refer to MATLAB documentation
%
% Arguments OUT
% P_AIB = PDF of A i.e incr1 conditioned on B i.e incr2
% P_BIA = PDF of B i.e incr2 conditioned on A i.e incr1
% P_AnB = Joint PDF of A i.e. incr1 and B i.e incr2
% P_A = PDF of A i.e incr1
% P_B = PDF of B i.e incr2
% binP_AIB = 2D binning 
% x_mean_bin = 1D array of centers of bins of incr2
% y_mean_bin = 1D array of centers of bins of inc
    
function
% varargin = Refer to MATLAB documentation
%
% Arguments OUT
% P_AIB = PDF of A i.e incr1 conditioned on B i.e incr2
% P_BIA = PDF of B i.e incr2 conditioned on A i.e incr1
% P_AnB = Joint PDF of A i.e. incr1 and B i.e incr2
% P_A = PDF of A i.e incr1
% P_B = PDF of B i.e incr2
% binP_AIB = 2D binning 
% x_mean_bin = 1D array of centers of bins of incr2
% y_mean_bin = 1D array of centers of bins of incr1
% events = number of elements that fall at the center in each bin
% counter_A = histogram of A
% counter_B = histogram of B
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function [P_AIB,P_BIA,P_AnB,P_A,P_B,binP_AIB,x_mean_bin,y_mean_bin,events,counter_A,counter_B] = distribution(tau1,tau2,incr1,incr2,num_bin,dx,dy,x,y,dA,varargin)
% P_AIB(counter_A>=min_events,counter_B>=min_events)
scatter                        = nan;  

% hist3 creates a bivariate histogram using x y binning
if tau1>tau2
    scatter(1:length(incr1),1)     = incr2(1,1:length(incr1));
    scatter(1:length(incr1),2)     = incr1(1,1:length(incr1));
    counter2                       = hist3(scatter,{y x}); 
else
    scatter(1:length(incr1),1)     = incr1(1,1:length(incr1));
    scatter(1:length(incr1),2)     = incr2(1,1:length(incr1));
    counter2                       = hist3(scatter,{x y}); 
end