probability vector造句
例句與造句
- Using this probability vector it is possible to create an arbitrary number of candidate solutions.
- This result is that the scaled probability vector is related to the backward probabilities by:
- The backward probability vectors above thus actually represent the likelihood of each state at time t given the future observations.
- Gibbs sampling will become trapped in one of the two high-probability vectors, and will never reach the other one.
- The logistic normal distribution is a more flexible alternative to the Dirichlet distribution in that it can capture correlations between components of probability vectors.
- It's difficult to find probability vector in a sentence. 用probability vector造句挺難的
- Multiplying u by that value gives a probability vector, giving the probability that the maximizing player will choose each of the possible pure strategies.
- A probabilistic forecaster or algorithm will return a probability vector "'r "'with a probability for each of the i outcomes.
- Another generalization that should be immediately apparent is to use a stochastic matrix for the transition matrices, and a probability vector for the state; this gives a probabilistic finite automaton.
- The "'logistic normal distribution "'is a generalization of the logit normal distribution to D-dimensional probability vectors by taking a logistic transformation of a multivariate normal distribution.
- We thus find that the product of the scaling factors provides us with the total probability for observing the given sequence up to time t and that the scaled probability vector provides us with the probability of being in each state at this time.
- We are here interested only in the equilibrium probability vector p ( \ infty ) \, given, in the usual way, by the dominant eigenvector of matrix P \, which is independent of the initialising vector p ( 0 ) \,.
- Finally, the Brouwer Fixed Point Theorem ( applied to the compact convex set of all probability distributions of the finite set \ { 1, . . ., n \ } ) implies that there is some left eigenvector which is also a stationary probability vector.
- If it is desired to inject this information into the model, the probability vector \ boldsymbol \ eta can be directly specified; or, if there is less certainty about these relative probabilities, a non-symmetric Dirichlet distribution can be used as the prior distribution over \ boldsymbol \ eta.
- This you then can represent as a probability vector [ 0.14, 0.17, . . ., 0.17, 0.18 ], which I've written sideways because I'm too lazy to make it vertical but in fact mathematically row vectors are often more convenient here.
- A stationary probability vector \ boldsymbol { \ pi } is defined as a distribution, written as a row vector, that does not change under application of the transition matrix; that is, it is defined as a probability distribution on the set \ { 1, . . ., n \ } which is also a row eigenvector of the probability matrix, associated with eigenvalue 1: