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{SECT 0 {PARA 18 "" 0 "" {TEXT -1 50 "Simulation d'une chaine de Marko
v en temps discret" }{MPLTEXT 1 0 0 "" }}{PARA 0 "> " 0 "" {MPLTEXT 1 
0 12 "with(stats);" }}{PARA 11 "" 1 "" {XPPMATH 20 "6#7*%&anovaG%)desc
ribeG%$fitG%+importdataG%'randomG%*statevalfG%*statplotsG%*transformG
" }}{PARA 0 "> " 0 "" {MPLTEXT 1 0 13 "with(linalg):" }}{PARA 7 "" 1 "
" {TEXT -1 80 "Warning, the protected names norm and trace have been r
edefined and unprotected\n" }}{SECT 0 {PARA 3 "" 0 "" {TEXT -1 33 "G
\351n\351rateurs de nombres al\351atoires" }}{SECT 0 {PARA 4 "" 0 "" 
{TEXT -1 17 "Loi exponentielle" }}{EXCHG {PARA 0 "> " 0 "" {MPLTEXT 1 
0 14 "lambda := 2.0;" }}{PARA 11 "" 1 "" {XPPMATH 20 "6#>%'lambdaG$\"#
?!\"\"" }}}{EXCHG {PARA 0 "> " 0 "" {MPLTEXT 1 0 39 "loi_exp := random
[exponential[lambda]];" }}{PARA 11 "" 1 "" {XPPMATH 20 "6#>%(loi_expG&
%'randomG6#&%,exponentialG6#$\"#?!\"\"" }}}{EXCHG {PARA 0 "" 0 "" 
{TEXT -1 78 "Renvoie un nombre al\351atoire distribu\351 selon la loi \+
exponentielle de param\350tre " }{XPPEDIT 18 0 "lambda" "6#%'lambdaG" 
}}}{EXCHG {PARA 0 "> " 0 "" {MPLTEXT 1 0 10 "loi_exp();" }}{PARA 11 "
" 1 "" {XPPMATH 20 "6#$\"+Uzr\\S!#6" }}}{EXCHG {PARA 0 "" 0 "" {TEXT 
-1 80 "Renvoie 5 nombres al\351atoires distribu\351s selon la loi expo
nentielle de param\350tre " }{XPPEDIT 18 0 "lambda" "6#%'lambdaG" }
{TEXT -1 1 " " }{MPLTEXT 1 0 0 "" }}}{EXCHG {PARA 0 "> " 0 "" 
{MPLTEXT 1 0 11 "loi_exp(5);" }}{PARA 11 "" 1 "" {XPPMATH 20 "6'$\"+0D
bX!*!#5$\"+!RbB*[F%$\"+9m]8=!#6$\"+b+UjLF*$\"+Qu=*)=F%" }}}}{SECT 0 
{PARA 4 "" 0 "" {TEXT -1 30 "G\351n\351rateurs de nombres entiers" }}
{EXCHG {PARA 0 "" 0 "" {TEXT -1 135 "Renvoie le nombre 1 avec la proba
bilit\351 prob[1], le nombre 2 avec la probabilit\351 prob[2], le nomb
re 3 avec la probabilit\351 prob[3]  etc." }}}{EXCHG {PARA 0 "> " 0 "
" {MPLTEXT 1 0 32 "prob := 0.0, 0.1, 0.5, 0.2, 0.2;" }}{PARA 11 "" 1 "
" {XPPMATH 20 "6#>%%probG6'$\"\"!F'$\"\"\"!\"\"$\"\"&F*$\"\"#F*F-" }}}
{EXCHG {PARA 0 "> " 0 "" {MPLTEXT 1 0 32 "alea := random[empirical[pro
b]];" }}{PARA 11 "" 1 "" {XPPMATH 20 "6#>%%aleaG&%'randomG6#&%*empiric
alG6'$\"\"!F,$\"\"\"!\"\"$\"\"&F/$\"\"#F/F2" }}}{EXCHG {PARA 0 "" 0 "
" {TEXT -1 27 "Renvoie 100 nombres entiers" }{MPLTEXT 1 0 0 "" }}}
{EXCHG {PARA 0 "> " 0 "" {MPLTEXT 1 0 10 "alea(100);" }}{PARA 12 "" 1 
"" {XPPMATH 20 "6`q\"\"%\"\"$F$\"\"&F%F$F$\"\"#F#F$F#F%F$F$F$F%F$F$F#F
$F$F&F&F#F%F$F$F$F%F$F#F$F%F&F$F$F#F$F&F&F$F#F#F$F#F#F%F$F%F$F$F#F&F$F
$F%F%F%F$F$F$F$F$F%F%F%F%F$F%F%F$F$F$F$F#F&F&F&F#F%F$F&F$F$F%F$F%F#F&F
$F$F$F$F$F#F#F$F#F$F&" }}}}}{SECT 0 {PARA 3 "" 0 "" {TEXT -1 50 "Simul
ation d'une chaine de Markov en temps discret" }}{EXCHG {PARA 0 "" 0 "
" {TEXT -1 13 "Fabrique une " }{HYPERLNK 17 "table" 2 "table" "" }
{TEXT -1 168 " Maple qui contient la matrice de transition P et le vec
teur stochastique initial v. Dans une version \351labor\351e, les donn
\351es pass\351es en param\350tre (P,v) sont control\351es. " }}}
{EXCHG {PARA 0 "> " 0 "" {MPLTEXT 1 0 69 "Chaine_Markov := proc(P::mat
rix, v::vector)\n   ERROR(\"\340 faire\");\nend:" }}}{EXCHG {PARA 0 "
" 0 "" {TEXT -1 276 "Proc\351dure de simulation d'une chaine de Markov
 pendant une certaine dur\351e (N pas de temps). Le r\351sultat de la \+
simulation est la liste des \351tats visit\351s. On pourra stocker cet
te liste dans un vecteur indic\351e de 0 \340 N. On passe en parametre
 une chaine de Markov et la dur\351e (N)." }}}{EXCHG {PARA 0 "> " 0 "
" {MPLTEXT 1 0 84 "simulation := proc(chaine_markov::table, duree::non
negint)\n  ERROR(\"a faire\"); \nend:" }}}}{EXCHG {PARA 0 "> " 0 "" 
{MPLTEXT 1 0 0 "" }}}{SECT 0 {PARA 3 "" 0 "" {TEXT -1 44 "V\351rificat
ion du temps pass\351 dans chaque \351tat" }}{EXCHG {PARA 0 "" 0 "" 
{TEXT -1 130 "Quand la condition d'ergodicit\351 est v\351rifi\351e, l
a distribution stationnaire fournit le pourcentage de temps pass\351 d
ans chaque \351tat." }{MPLTEXT 1 0 0 "" }}}}}{MARK "6 4 0 0" 39 }
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