#N canvas 296 90 662 442 12;
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-1;
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#X msg 81 358 1;
#X msg 162 360 2;
#X msg 199 361 3;
#X obj 81 386 s state;
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#X obj 105 164 r state;
#X obj 83 225 sel 1 2 3;
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#X msg 252 361 1;
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#X msg 373 367 3;
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#X obj 499 338 bng 15 250 50 0 empty empty empty 20 8 0 8 -262144 -1
-1;
#X obj 538 341 bng 15 250 50 0 empty empty empty 20 8 0 8 -262144 -1
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#X msg 419 362 1;
#X msg 499 363 2;
#X msg 538 364 3;
#X obj 418 395 s state;
#X msg 236 186 \; state 1;
#X obj 83 199 f 1;
#X obj 84 279 random 100;
#X obj 83 308 moses 30;
#X obj 162 309 moses 60;
#X obj 255 280 random 100;
#X obj 255 310 moses 10;
#X obj 334 311 moses 60;
#X obj 419 281 random 100;
#X obj 419 310 moses 70;
#X obj 499 310 moses 80;
#X floatatom 134 188 3 0 0;
#X text 236 166 reset;
#X text 49 152 STEP;
#X text 34 20 Here is how to construct a simple \, three-valued Markov
chain using "random." Each time you click on "step" the previous output
("state") determines which of three random networks to invoke \, each
having a different probability distribution for the next value of "state."
For instance if the state was 3 \, the next state will be 1 70% of
the time \, state 2 10% \, and state 3 20%.;
#X text 408 422 updated for Pd version 0.35;
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