Stanford Mathematical Studies in the Social Sciences, Volume 8 |
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Page 311
... measure e ; may be identified with many physical processes . It is also for this reason that it is called elemen- tary , and it will enter as a component into " more advanced " measures , e.g. , scales . When we begin to measure , for ...
... measure e ; may be identified with many physical processes . It is also for this reason that it is called elemen- tary , and it will enter as a component into " more advanced " measures , e.g. , scales . When we begin to measure , for ...
Page 312
... measure easier . We now return to the problem of sequentially applying two elementary measures . Let the elementary measures be e , and e , and let e , ▷ e , denote applying first e , and then e , to the result ; i.e. , ( 4.5 ) e2 ...
... measure easier . We now return to the problem of sequentially applying two elementary measures . Let the elementary measures be e , and e , and let e , ▷ e , denote applying first e , and then e , to the result ; i.e. , ( 4.5 ) e2 ...
Page 314
... measures we can apply in a bounded amount of time , and hence distinguishability is limited by the set of measures we choose to use . We can further weaken what we mean by two states being distinguishable . It seems reasonable to define ...
... measures we can apply in a bounded amount of time , and hence distinguishability is limited by the set of measures we choose to use . We can further weaken what we mean by two states being distinguishable . It seems reasonable to define ...
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A₁ alternative analysis applied Asch assumption asymptotic Atkinson AU's autotelic axioms behavior binomial distribution C₁ C₂ cent choice coalition concept conditional probabilities consider correct defined deontic logic depend described discussion distribution dyadic effect elementary equations estimate example function game theory given group members individual learning experiments learning models linear model Markov Markov chain mathematical matrix mean measures minimax modèle observed obtained occurs outcome P(A₁ P(Yes p₁ pair paper paradigm parameters payoff person plausibilités player possible prediction present probabilités problem proportion Psychol punishment R₁ random variables recursions reference group reinforcing events rejection relation relationship réponse response probabilities reward S₁ S₂ sequence simple social stimulus solution Stanford statistical learning theory stimulus stochastic structure subjects sujet Suppes task theoretical tion transition probabilities trial trial n two-person interactions Univ values zero-sum zero-sum game