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Lecture 5

STAT 455 Lecture 5: Lecture 9 & 10
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4 Pages
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Department
Statistics
Course Code
STAT 455
Professor
Glen Takahara

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Description
O et. 4201 -7ransttiou Probability ntl /matrix ︾uLNIL1.tudiuhdaa/s @ qu'ention t- nャ, 米notjust ovu step to toefdue but n steps to tte fbture matrrxWst n-step -rensitton matrix ton yMartoy Peopety, espm-step Ton Should be tHe sane s s true for any i.je ik(M) keS matrix multiplicecton datewnas au we can now determine any fri nite-dmensional dist. Ret n, c n····suk be 1 all step Trans n Han P plus tu ost He Morkov Chain Xo determine all tu finite dmausbowal dst ot a of clhoin Tlwe is a theorem by Kolmogarou states that Ha dst. is m fact detemred : we s ue say ther ste j is | accessible) foun stote i -f Pei(n) >0 O et. 4201 -7ransttiou Probability ntl / matrix ︾uLNIL1.tudiuhdaa / s @ qu'ention t- n ャ , 米 notjust ovu step to toefdue but n steps to tte fbture matrrxWst n - step - rensitton matrix ton yMartoy Peopety, espm-step Ton Should be tHe sane s s true for any i.je ik ( M ) keS matrix multiplicecton datewnas au we can now determine any fri nite - dmensional dist . Ret n , c n ···· suk be 1 all step Trans n Han P plus tu ost He Morkov Chain Xo determine all tu finite dmausbowal dst ot a of clhoin Tlwe is a theorem by Kolmogarou states that Ha dst. is m fact detemred : we s ue say ther ste j is | accessible ) foun stote i - f Pei ( n ) > 0Oct.SH..201 구 9.52 → Structure of a Markov Chain . のTwo states讠& . communicate tare ost m & n st. PT (m) >0 R it rt tisfes tu folloung 3 popertes a subset ef Subset Stnue e RHS
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