MCTPSIM Simulates a Markov chain of turning points The process x has the state space {1,2,...,n}. CALL: [x] = mctpsim(F,T); [x] = mctpsim(F,T,init); [x] = mctpsim(F,T,init,'x'); [RFM,RFM0,res] = mctpsim(F,T,init,'RFM'); [x,RFM] = mctpsim(F,T,init,'x,RFM'); x = Simulated switching Markov turning points. RFM = Rainflow matrix for x. [nxn] RFM0 = Rainflow matrix for x (without the residual). [nxn] res = Residual from rainflow count. [nx2] F = Cell array of min-Max and Max-min matrices {1,2} F{1,1} = min-Max matrix, process 1 [nxn] F{1,2} = Max-min matrix, process 1 [nxn] T = Length of simulation. init.x0 = Initial state of process x. If not given, it will start from the stationary distribution of minima. Simulates a Markov chain of turning points, The process x has the state space {1,2,...,n}. If a matrix F{1,2}=[], then the process will be assumed to be time-reversible. Examples: FF = mktestmat([-1 1 32],[-0.2 0.2],0.15,1); x = mctpsim({FF []},1000);
Simulates a switching Markov chain of turning points, |
% CHAPTER4 contains the commands used in Chapter 4 of the tutorial | |
Script to computer exercises 2 | |
Script to computer exercises 3 | |
Quick test of the routines in module 'cycles' | |
Quick test of the routines in module 'markov' |
001 function [x,out2,out3] = mctpsim(varargin) 002 %MCTPSIM Simulates a Markov chain of turning points 003 % The process x has the state space {1,2,...,n}. 004 % 005 % CALL: [x] = mctpsim(F,T); 006 % [x] = mctpsim(F,T,init); 007 % [x] = mctpsim(F,T,init,'x'); 008 % [RFM,RFM0,res] = mctpsim(F,T,init,'RFM'); 009 % [x,RFM] = mctpsim(F,T,init,'x,RFM'); 010 % 011 % x = Simulated switching Markov turning points. 012 % RFM = Rainflow matrix for x. [nxn] 013 % RFM0 = Rainflow matrix for x (without the residual). [nxn] 014 % res = Residual from rainflow count. [nx2] 015 % 016 % F = Cell array of min-Max and Max-min matrices {1,2} 017 % F{1,1} = min-Max matrix, process 1 [nxn] 018 % F{1,2} = Max-min matrix, process 1 [nxn] 019 % T = Length of simulation. 020 % init.x0 = Initial state of process x. If not given, it will start from 021 % the stationary distribution of minima. 022 % 023 % Simulates a Markov chain of turning points, 024 % The process x has the state space {1,2,...,n}. 025 % 026 % If a matrix F{1,2}=[], then the process will 027 % be assumed to be time-reversible. 028 % 029 % Examples: 030 % FF = mktestmat([-1 1 32],[-0.2 0.2],0.15,1); 031 % x = mctpsim({FF []},1000); 032 033 init.z0=1; 034 035 [x,out2,out3] = smctpsim(1,varargin{:}); 036 %switch whatOut 037 038 %case {'x'} 039 040 % x = smctpsim(1,vargin{:}); 041 042 %case {'RFM'} 043 044 % [x,out2,out3] = smctpsim(1,vargin{:}); 045 046 %case {'x','x,RFM'} 047 048 % [x,out2] = smctpsim(1,vargin{:}); 049 050 %end 051
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