weak convergence वाक्य
उदाहरण वाक्य
मोबाइल
- Convergence in probability is also called weak convergence of random variables.
- The definition of weak convergence can be extended to Banach spaces.
- As the names indicate, weak convergence is weaker than strong convergence.
- If a function is weak convergence is usually of interest.
- Convergence in total variation is stronger than weak convergence.
- Weak convergence is also called "'convergence in distribution " '.
- This is the weak convergence of laws without laws being defined except asymptotically.
- In fact, strong convergence implies convergence in probability, and convergence in probability implies weak convergence.
- The modern theory still carries the imprint . . . [ This concerns weak convergence in arrays ].
- This definition of weak convergence can be extended for " S " any metrizable topological space.
- Kantorovich considered infinite-dimensional optimization problems, such as the Kantorovich-Monge problem in weak convergence of probability measures.
- The discussion is mainly based on the book " Weak Convergence and Empirical Processes : With Applications to Statistics ".
- There are several equivalent definitions of weak convergence of a sequence of measures, some of which are ( apparently ) more general than others.
- His research interests also include measure-theoretical analysis, weak convergence methods, stochastic partial differential equations, statistical physics, and numerical analysis.
- *( see p . 7 for weak convergence, p . 24 for convergence in distribution and p . 70 for Skorokhod's theorem)
- The terms are uniformly bounded in L p, so to check weak convergence to 0 it enough to check inner products with a dense subset of L 2.
- For instance, stochastic equicontinuity, along with other conditions, can be used to show uniform weak convergence, which can be used to prove the convergence of extremum estimators.
- Furthermore, convergence with respect to " W " " p " is equivalent to the usual weak convergence of measures plus convergence of the first " p " th moments.
- More generally, if the u . a . n . condition is satisfied via a scaling of identically distributed random variables ( with not necessarily finite second moment ), then the weak convergence is to a stable distribution.
- The Delta-compactness theorem is similar to the Banach Alaoglu theorem for weak convergence but, unlike the Banach-Alaoglu theorem ( in the non-separable case ) its proof does not depend on the Axiom of Choice.
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