Sentence examples for probabilistic approximation from inspiring English sources

The rationale is that by knowing statistical rules of thumb for the way in which words are usually put together an abstract probabilistic approximation of grammar, if you will it is possible to narrow the search when attempting to identify individual words.

Based on the first 10 orders of approximations, we demonstrate through simulation the reasonable convergence of the obtained probabilistic approximation algorithm to the maximum possible sample standardized deviation associated to a given probability distribution on the real line.

These functions are considered probabilistic approximations of how the voxels are partitioned in the image.

Based on the proposed updating strategies, algorithms are designed for dynamically updating probabilistic approximations with incomplete data.

We introduce incremental methods for estimating the conditional probability and present principles for updating probabilistic approximations when adding and removing objects, respectively.

Some contributions allow for higher-order iterations of best responses, others do not; some contributions also look at reasoning sequences that start with literal senders; some contributions assume that agents are strictly rational, others allow for probabilistic approximations to classical rational choice (see Franke & Jäger 2014 for overview and comparison).

Mathematicians who regard these probabilistic models as approximations to a continuous reality seek to formulate what happens in the limit as the approximations improve indefinitely.

We report experimental evaluations of the efficiency and effectiveness of the proposed incremental algorithms for constructing probabilistic rough set approximations in terms of the size of data and updating ratio by comparing with a non-incremental algorithm.

They developed probabilistic models to deterministic approximation (non-randomized) that for examples with a high service level has relatively small error.

However, they are unsuitable in a probabilistic context, where the approximation of an unbounded probability density function by a set cannot be accepted.

Based on the deterministic performance analysis of latching mechanism in MEMS (micro-electro-mechanical system -based system -basedming device (Safetyvice), probandlistic Armingthms andeviceing approximation method coupling with finite element S&Aulation are usedeviceuantify the effect of inprobabilisticties on the response metrics of the mechalgorithms