Sentence examples for complex probability from inspiring English sources

Our process-based, statistical approach gives skewed and complex probability distributions (single mode, 10 cm, at 2100; two modes, 49 cm and 6 cm, at 2200).

This week we're spinning the defeat – the playoffs are still possible, but complex probability equations assert that 20 more points are needed from the remaining 10 games to make the cutoff for the post season.

Using a complex probability approach, Weusten et al.28 have proposed the symptom association probability (SAP), arguing that this parameter overcomes some of the limitations of the symptom index and symptom sensitivity index.

Sampling tests carried for the case of Molière electron multi-scatter angle distribution show that this method can be successfully used in Monte Carlo codes for sampling complex probability distributions.

The multi-try differential evolution adaptive Metropolis (MT-DREAMzs) algorithm is effective and robust in searching complex probability space, and it is incorporated into NSE to improve the performance of marginal likelihood estimation in this study.

Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches.

We also need to think hard about the kind of genomic information it is appropriate to share directly with patients and doctors, and how they can be helped to interpret the complex probabilities it might reveal.

Estimating these complex probabilities is straightforward in an MCMC setting.

Excitingly, some predictions of hyper-complex are experimentally testable, since basing the superposition principle on hyper-complex probability amplitudes leads to a version of quantum mechanics wherein simple phases are not guaranteed to commute32,33.

This stage, called Extract, consists of two steps (see Figure 1): first, the small-co-complex probability weight of each edge is disambiguated into size-2 and size-3 complex components; next, each candidate complex is scored by its cohesiveness-weighted density, which is based on both its internal and outgoing edges.

In the second step, our complex extraction approach, called Extract, uses these weights to predict and score candidate small complexes, by weighting their densities with a cohesiveness function [ 5] that incorporates both small and large co-complex probabilities of edges within and around each cluster.