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credal
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The answer CREDAL has 2 possible clue(s) in existing crosswords.
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The word CREDAL is VALID in some board games. Check CREDAL in word games in Scrabble, Words With Friends, see scores, anagrams etc.
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Definitions of credal in various dictionaries:
adj - of or relating to a creed
adj - a statement of belief [n -S] : CREEDAL
CREDAL - A credal set is a set of probability distributions or, more generally, a set of (possibly finitely additive) probability measures. A credal set is o...
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Keep reading for additional results and analysis below.
Possible Crossword Clues |
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To do with set of principles Ruby found in California |
Of a system of belief |
Last Seen in these Crosswords & Puzzles |
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Aug 5 2018 The Telegraph - General Knowledge |
Jul 17 2010 The Telegraph - Cryptic |
Possible Dictionary Clues |
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Variant of creedal. |
of or relating to a creed |
Relating to a statement of Christian or other religious belief. |
Credal might refer to |
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A Credal set is a set of probability distributions or, more generally, a set of (possibly finitely additive) probability measures. A credal set is often assumed or constructed to be a closed convex set. It is intended to express uncertainty or doubt about the probability model that should be used, or to convey the beliefs of a Bayesian agent about the possible states of the world.If a credal set * * * * K * ( * X * ) * * * {\displaystyle K(X)} * is closed and convex, then, by the Krein–Milman theorem, it can be equivalently described by its extreme points * * * * * e * x * t * * [ * K * ( * X * ) * ] * * * {\displaystyle \mathrm {ext} [K(X)]} * . In that case, the expectation for a function * * * * f * * * {\displaystyle f} * of * * * * X * * * {\displaystyle X} * with respect to the credal set * * * * K * ( * X * ) * * * {\displaystyle K(X)} * forms a closed interval * * * * [ * * * E * _ * * * [ * f * ] * , * * * E * ¯ * * * [ * f * ] * ] * * * {\displaystyle [{\underline {E}}[f],{\overline {E}}[f]]} * , whose lower bound is called the lower prevision of * * * * f * * * {\displaystyle f} * , and whose upper bound is called the upper prevision of * * * * f * * * {\displaystyle f} * :* * * * * * E * _ * * * [ * f * ] * = * * min * * μ * ∈ * K * ( * X * ) * * * ∫ * f * * d * μ * = * * min * * μ * ∈ * * e * x * t * * [ * K * ( * X * ) * ] * * * ∫ * f * * d * μ * * * {\displaystyle {\underline {E}}[f]=\min _{\mu \in K(X)}\int f\,d\mu =\min _{\mu \in \mathrm {ext} [K(X)]}\int f\,d\mu } * where * * * * μ * * * {\displaystyle \mu } * denotes a probability measure, and with a similar expression for * * * * * * E * ¯ * * * [ * f * ] * * * {\displaystyle {\overline {E}}[f]} * (just replace * * * * min * * * {\displaystyle \min } * by * * * * max * * * {\displaystyle \max } * in the above expression). * If * * * * X * * * {\displaystyle X} * ... |