Nebenbedingungen Ls´  ,P s´ =−∑ S P s´ s∣o⋅log2P s´ s∣o∑ a a∑S f ao ,s ⋅Ps´ s∣o−f ao ,sk Erste Ableitung nach P bilden und gleich Null setzen: Ls´  ,P s´  P s´ s∣o  =−1−log2Ps´ [...] s∣o ⋅log2P s ´ s∣o  bei gegebenem o Ziel: Maximierung dieser Funktion unter den Nebenbedienungen: LAGRANGE f ao ,sk =∑ S f ao ,s ⋅P s ´ s∣o ∀s ´ ,sk ,a bei gegebenem o 1=∑ S P s ´ s∣o ∀s [...] Roman Organi- sation 2. Übersetzung aus dem englischen: by an bis mit 0,9 0,1 0,3 0,5 0,2 S O O S Zahlen sind die Wahscheinlickeiten P s ,o Maximum Entropy Markov Models for Information Extraction and …

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c) For zn holds: ∀z ∈ Z ∀p ∈ P : ((zn, z) ∈→ ⇒ (zn(p) = z(p) ∨ (zn(p) = 1 ∧ z(p) ∈ P ))). ⇒ {logical transformation} ∀z ∈ Z ∀p ∈ P : ((zn, z) /∈→ ∨zn(p) = z(p)∨(zn(p) = 1∧z(p) ∈ P )). ⇒{Definition 12; only [...] • K ⊆ P × P is the coupling relation of IS where K is symmetrical and ∀b ∈ B ∀p, p′ ∈ b : p 6= p′ ⇔ (p, p′) ∈ K. • E ⊆ P × P is the excitement relation of IS with E ∩ (E−1 ∪K) = ∅. For p ∈ P , b(p) denotes [...] j < k ∈ IN ∃p, q ∈ P : • zi(p) = zi+1(p) = . . . = zj−1(p) = 1 and zj(p) = zj+1(p) = . . . = zk−1(p) = F and zk(p) = 1, • ∃v ∈ P : (v, p) ∈ E, and v is occupied in zi, • ∃v′ ∈ P : (p, v′) ∈ E, and v′ is …

cora.dvi

MP1(S,P1,R): S(Est) & P1(Sit) --> R(Sit,Est). MP2(S,P1,P2,R): S(Est) & P1(Sit) & P2(Sit) --> R(Sit,Est). MP3(S,P1,P2,P3,R): S(Est) & P1(Sit) & P2(Sit) & P3(Sit) --> R(Sit,Est). MP4(S,P1,P2,P3,P4,R): S(Est) [...] follows: P P P 0 ; i min(f(X)jXoccurring inPg) min(f(X)jXoccurring inP 0 g). While instantiating the premises of the rule schema with respect to this order we instantiate P before P 0 , if P P P 0 . Then [...] interest. Let us denote the probability for a case being exposed by p 1 and for a control p 0 . Then, the odds ratio is dened as p 1 (1p 0 ) p 0 (1p 1 ) . Typically, there are additional confounding variables …