Examples of using Bayesian in English and their translations into Russian
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Bayesian Economics Through Numerical Methods.
The purpose of Bayesian programming is different.
Credence is especially important in Bayesian statistics.
Bayesian email filters utilize Bayes' theorem.
John C. Harsanyi describes a Bayesian game in the following way.
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An HMM can be considered as the simplest dynamic Bayesian network.
In particular, Bayesian networks and random fields are popular.
The analysis design is cross-sectional multilevel regression with Bayesian estimation.
Judea Pearl creates Bayesian networks that mimic human behaviour.
Bayesian programming is a formal and concrete implementation of this"robot.
One of the exceptions is the Bayesian Methods Research Group led by Prof. Dmitry Vetrov.
A Bayesian program is a means of specifying a family of probability distributions.
The use of Bayes factors is a Bayesian alternative to classical hypothesis testing.
Bayesian model comparison is a method of model selection based on Bayes factors.
The formal description of the model of control is a Bayesian game with two decision-makers.
Practical Bayesian Optimization of Machine Learning Algorithms.
Selecting the network parameters by using optimization algorithms,for example, Bayesian.
The purpose of Bayesian spam filtering is to eliminate junk e-mails.
Different scenarios are evaluated through effective computational Bayesian networks algorithms.
The first scholarly publication on Bayesian spam filtering was by Sahami et al. in 1998.
Bayesian junk-mail filtering and top-level domain blocking and encoding blocking have been added.
Pocomail also includes a strong Bayesian filtering engine for preventing spam.
The Bayesian design of experiments includes a concept called'influence of prior beliefs.
The Student's t-distribution also arises in the Bayesian analysis of data from a normal family.
Bayesian optimization is a global optimization method for noisy black-box functions.
A new mathematical tool, developed in 2010,allows you to design scalable Bayesian models.
Neural networks and Bayesian models are two popular paradigms in the field of machine learning.
The product is used More than 20 different technologies to combat spam: Bayesian, SpamAssassin, URIBL/ SURBL, RBL and so on.
In a Bayesian context, this is equivalent to the prior predictive distribution of a data point.