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1                                              Bernoulli mixture-model clustering was applied and the r
2                                              Bernoulli's equation relates differences between P(IA) (
3 e data with predictions of two models: (1) a Bernoulli Model in which bases are assumed equally likel
4 h the figure predicted by modelling DNA as a Bernoulli stream or as a Markov chain, using windows of
5 stributed (i.i.d.) random variables, i.e., a Bernoulli sequence.
6 dren were suggested using information from a Bernoulli naive Bayes classifier relying on the human ph
7 er-state generalized linear models (GLMs): a Bernoulli GLM for state- and stimulus-dependent choices,
8 te Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights.
9                               We introduce a Bernoulli-lognormal mixture model for clustering DNA met
10      Combining this measurement model with a Bernoulli prior over binary spike trains yields a poster
11 ine, random forest, Gaussian naive Bayes and Bernoulli naive Bayes for separating infectious from non
12 the standard elastic rod theory of Euler and Bernoulli, or even the more general Cosserat theory of r
13 s: multinomial, Poisson, hypergeometric, and Bernoulli product.
14 ers claim that human behavior in a two armed Bernoulli bandit task is described by positivity and con
15    An idealized adaptive case--the two-armed Bernoulli bandit problem--can be exactly optimized for a
16 hese components (spaces) inferred via a beta-Bernoulli process.
17 red premature moulting in juvenile copepods (Bernoulli GLM, P < 0.01).
18            In the eighteenth century, Daniel Bernoulli, Adam Smith and Jeremy Bentham proposed that e
19  driver in this situation is the depression (Bernoulli) wake that may be transferred into a long-livi
20 inear and non-linear machine-learning (i.e., Bernoulli, support vector, random forest and Gaussian pr
21 probability of identifying a protein at each Bernoulli event is determined from relative length of th
22 robability of protein identification at each Bernoulli event.
23                       Finally, an AI-enabled Bernoulli Deep Belief Network is designed to predict the
24 myloid fibrils, suggesting that simple Euler-Bernoulli beam models fail to describe the mechanics of
25  similar to the ratio predicted by the Euler-Bernoulli theorem for linear cantilevers.
26 ief Network, Xavier Initialization function, Bernoulli distribution function, and Principal Component
27  1-variable logistic map and the generalised Bernoulli map with various number formats and precisions
28  that each analyte represents an independent Bernoulli random variable, which is then used to predict
29 e MRF prior to a situation where independent Bernoulli priors are chosen for the individual predictor
30 ound on the information lost when spiking is Bernoulli in discrete time bins.
31 ve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naive Bayes (BNB) and decision tree (DT) class
32 ents were estimated by means of the modified Bernoulli equation, and VTIs were calculated to estimate
33 e PASP was calculated by use of the modified Bernoulli equation, with right atrial pressure assumed t
34   In this article, we apply the multivariate Bernoulli (MVB) distribution to model haplotype data.
35 ur order-2 approximation of the multivariate Bernoulli distribution.
36 fferent parametrizations of the multivariate Bernoulli distribution.
37            AI-enabled Barilai-Blinder-Oaxaca-Bernoulli Deep Classifier (BBO-BDC) is proposed includin
38 each variable were calculated as the mean of Bernoulli random variables and for the risk estimate, by
39 nalysis allowing for normally distributed or Bernoulli distributed exposures, outcomes, mediators, me
40 , commonly taken to be 4.0 in the simplified Bernoulli equation delta P = KV2, was a function of sten
41 sumption that the target is strand-symmetric Bernoulli text (i.e. nucleotides are independently, iden
42 ming the continuum hypothesis, we prove that Bernoulli function(H) has a pure state whose restriction
43                                          The Bernoulli event has two outcomes: a protein is either id
44                                          The Bernoulli-lognormal mixture assigns observations to subg
45 erent models: (1) the Poisson model, (2) the Bernoulli model and (3) the zero-truncated Poisson model
46         From the calculated loading, and the Bernoulli-Euler curvature and moment equation, we find t
47 appropriately model interactions between the Bernoulli random variables.
48                                 However, the Bernoulli formulation demonstrated a consistent overesti
49                                 However, the Bernoulli principle relies on several approximations tha
50 yfish is due to suction pumping, and not the Bernoulli effect.
51 ribution using the unsteady flow form of the Bernoulli equation and was compared to the catheter meas
52            Neither (1) simplification of the Bernoulli equation nor (2) pressure recovery effects can
53                       The application of the Bernoulli formulation results in a clinically significan
54  METHODS AND We assessed the accuracy of the Bernoulli principle to estimate the peak pressure drop a
55 nd 6 different normalization techniques, the Bernoulli naive Bayes model after standardizing files ha
56  noninvasively by echocardiography using the Bernoulli principle.
57 ed into a cantilever-in-mass model using the Bernoulli-Euler beam theory.
58 gths </= 3 are in general agreement with the Bernoulli Model.
59 aluated using three different beam theories: Bernoulli-Euler (BE) or Classical Beam Theory (CBT), Tim
60 cal model is developed based on the unsteady Bernoulli equation and can well predict the ending point
61 uld then be integrated to yield the unsteady Bernoulli equation and estimate noninvasively both the c