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mctpsim

PURPOSE ^

Simulates a Markov chain of turning points

SYNOPSIS ^

[x,out2,out3] = mctpsim(varargin)

DESCRIPTION ^

 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);

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

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

Mathematical Statistics
Centre for Mathematical Sciences
Lund University with Lund Institute of Technology

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