例句與造句

1. These two simple matrices allow us to represent three special cases of linear belief functions.
2. There are two basic operations for making inferences in expert systems using linear belief functions : combination and marginalization.
3. Also, note that a vacuous linear belief function ( 0 swept matrix ) is the neutral element for combination.
4. Here we use it to define the combination of two linear belief functions, which include normal distributions as a special case.
5. By using the fully swept moment matrix, we represent the vacuous linear belief functions as a zero matrix in the swept form follows:
6. It's difficult to find linear belief function in a sentence. 用linear belief function造句挺難的
7. For this reason, a better way is to understand the vacuous linear belief functions as the neutral element for combination ( see later ).
8. "' Linear belief function "'is an extension of the Dempster Shafer theory of belief functions to the case when variables of interest are continuous.
9. They also form the basis of the moment matrix representations for the three remaining important cases of linear belief functions, including proper belief functions, linear equations, and linear regression models.
10. A linear belief function can represent both logical and probabilistic knowledge for three types of variables : deterministic such as an observable or controllable, random whose distribution is normal, and vacuous on which no knowledge bears.
11. Note that the knowledge to be represented in linear equations is very close to that in a proper linear belief functions, except that the former assumes a perfect correlation between X and Y while the latter does not.
12. In Dempster Shafer theory, each state equation or observation is considered a special case of a linear belief function and the Kalman filter is a special case of combining linear belief functions on a join-tree or Markov tree.
13. In Dempster Shafer theory, each state equation or observation is considered a special case of a linear belief function and the Kalman filter is a special case of combining linear belief functions on a join-tree or Markov tree.
14. A linear belief function is a special type of belief function in the sense that its focal elements are exclusive, parallel sub-hyperplanes over the certainty hyperplane and its mass function is a normal distribution across the sub-hyperplanes.
15. which is not the same linear belief function of Y . However, it is easy to see that removing any or all variables in Y from the partially swept matrix will still produce the correct result  a matrix representing the same function for the remaining variables.
16. A linear belief function intends to represent our belief regarding the location of the true value as follows : We are certain that the truth is on a so-called certainty hyperplane but we do not know its exact location; along some dimensions of the certainty hyperplane, we believe the true value could be anywhere from   " to +  " and the probability of being at a particular location is described by a normal distribution; along other dimensions, our knowledge is vacuous, i . e ., the true value is somewhere from   " to +  " but the associated probability is unknown.