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    The properties of epistemic-probability models imply that... — Carmelics
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    Supports→Specifying an epistemic-probability model requires only a prior probability and a partition for each agent, rather than a separate probability measure for each partition cell.

    The properties of epistemic-probability models imply that each agent's probability measures arise from one prior through conditionalization.

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    Given a prior p_i and a partition Pi_i such that p_i(Pi_i(w)) > 0 for each state...Specifying an epistemic-probability model requires only a prior probability and ...This reduces the specification burden compared to independently defining a proba...

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    Specifying an epistemic-probability model requires only a prior probab...88%For each agent, the agent's probability measures across partition cell...85%These two properties together constrain the probability measures acros...81%Fiducial probability can give classical statistics an epistemic status78%

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    SEP: epistemic-game
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    Property 1 says that if \(i\) assigns a non-zero probability to state \(v\) at state \(w\) then the agent uses the same probability measure at both states. This means that the players “know” their own probability measures. The second property implies that players must assign a probability of zero to all states outside the current (hard) information cell. These models provide a very precise description of the players’ hard and soft informational attitudes. However, note that writing down a model

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