More sophisticated risk assessment methods, using probabilistic models to study variability in pesticide residue levels and food consumption data, would be needed to achieve this task.
We prove, via different methods using probabilistic techniques or PDE arguments, that Tμt is ultracontractive, i.e., for t > 0 it maps L1 into L∞ when the function u satisfies a growth condition at infinity, which is essentially (for instance when the dimension d = 1) the integrability of 1/u′ at infinity.
HMMER implements methods using probabilistic models called profile hidden Markov models (profile HMMs).
With the ability to report confidence for processed complex events over uncertain data, this method uses probabilistic nondeterministic finite automaton and active instance stacks to process a complex event in both single and distributed probabilistic event streams.
In this article, we describe a speaker adaptation method using probabilistic 2-mode analysis, which is an application of probabilistic tensor analysis (PTA)[8] to the second-order tensor (i.e., matrix); PTA is an application of probabilistic PCA (PPCA)[9] to tensor objects.
Context recognition algorithms based on supervised and unsupervised learning methods primarily use probabilistic and statistical reasoning.
While these methods increasingly use probabilistic modeling, including HMMs (Ha et al., 2012), to account for noisy data, they do not infer individual subclonal fractions and copy-number profiles and often include only one or two of the available data types.
Geneticists use probabilistic methods to map mutations onto specific tree branches.
At the given scale there is absolutely no reason not to use probabilistic methods of inference that are easily available (like EREM, COUNT or GLOOME; a large family of Bayesian inference methods can also be adapted to the task).
In contrast to other robust methods, it uses probabilistic description of uncertain system parameters and consequently, stochastic optimization methods are used to design the controller.
