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We define conditional simplexes and examine their basic properties.
Following the previous work [6],[16],[22], we define (conditional) expectations as shown below.
Unlike probabilists, De Finetti uses a primitively given notion of information in order to define conditional probability.
With such a family of maps, one can effectively define conditional probabilities within a single state space, and then translate them into "transition" probabilities.
The second representation uses the output of a linear filter with random coefficients subjected to Gaussian noise to define conditional Gaussian processes.
For this problem we need to extend traditional constraint-based scheduling techniques in two directions: (i) compute the probability of sets of scenarios in polynomial time, in order to get the expected value of the objective function; (ii) define conditional constraints that ensure feasibility in all scenarios.
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In addition, PV provides two ways of defining conditional probability P, which are negative sampling (NS) and hierarchical softmax (HS).
Then, based on the defined conditional probability in this paper, we design a multi-granulation variable precision rough set and consider the relationship with MGRS.
Bayes' Theorem defines conditional probability ‒ "how information about one event can give us understanding of another"; as such it offers a great improvement over the intuition or guesswork on which most of us tend to rely when relating one thing to another.
Spatial Markov chain models use the full independence assumption and the conditional independence assumption to define the conditional probability for simplifying the complex computation in Eq. (3).
Conditional independence statements, encoded by the network structure, define the conditional probability distribution.
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