ライフサイエンスコーパス: Bayesian

1 24-h systolic blood pressure was -3.9 mm Hg (Bayesian 95% credible interval -6.2 to -1.6) and for off

2 by enriching both sociocultural theories and Bayesian accounts of cognition.

3 derweighted in autism, as proposed by simple Bayesian accounts of the disorder.

4 ment (the "slow world" prior) as premised by Bayesian accounts.

5 Manning builds an inappropriate Bayesian age model to assert that the initial occupation

6 We propose a sparsity-promoting Bayesian algorithm capable of identifying radionuclide s

7 PET images using the fuzzy locally adaptive Bayesian algorithm, and manually in the low-dose CT imag

8 Bayesian analyses confirmed the spread of the NRCS-A clo

9 three nuclear genetic loci, and incorporated Bayesian analyses to resolve geographically distinct tic

10 Using conjunction, contrast, and Bayesian analyses, we demonstrate that unexpected action

11 ce and size effects in either frequentist or Bayesian analyses.

12 Both a post hoc Bayesian analysis and a mixed logistic regression analys

13 relocalisation with organelle proteomics and Bayesian analysis to define the content of different end

14 tworks.Measurements and Main Results: In the Bayesian analysis, the posterior probability that a peri

15 (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R-I

16 In comparative benchmarking with Bayesian and non-Bayesian methods (LSD, TreeTime, and tr

17 We tested Bayesian and RelTime approaches that do not require a mo

18 sted Circulatory Support, Kormos, Pittsburgh Bayesian, and Mechanical Circulatory Support Research Ne

19 D occurred in 10.3% of these patients, and a Bayesian approach (BeviMed(4)) identified multiple new c

20 ss this issue, in this study, an alternative Bayesian approach (Integrated Nested Laplace Approximati

21 Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq

22 We employed a hierarchical Bayesian approach that incorporated gross primary produc

23 We evaluate the new Bayesian approach using gamma-ray data and are able to i

24 Here, we present a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothi

25 Here, we present a new Bayesian approach, PathFinder, for reconstructing the ro

26 We fitted the model, using a hierarchical Bayesian approach, to experimental time-series data of a

27 ikelihood framework whereas STRUCTURE uses a Bayesian approach, yet both produce similar results.

28 with associated uncertainties, obtained by a Bayesian approach.

29 was predicted by a computationally demanding Bayesian approach.

30 ediction error and precision signaling using Bayesian approaches.

31 We demonstrate that our Bayesian-based data-model assimilation technique is able

32 The Bayesian brain hypothesis, as formalized by the free-ene

33 But, how does the Bayesian brain obtain prior beliefs?

34 f predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updat

35 Naive Bayesian classification had similar results on the combi

36 est-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection

37 speech articulation, as accounted for by the Bayesian classifier.

38 for pseudotime inference with non-parametric Bayesian clustering methods, efficient Markov Chain Mont

39 yline plots (BSP), multivariate analyses and Bayesian clustering.

40 chology of reasoning in the wider context of Bayesian cognitive science.

41 ostic algorithm was established based on the Bayesian combination of pretest probability and likeliho

42 for the Andaman day gecko using Approximate Bayesian Computation (ABC) supports two possible scenari

43 We also included Approximate Bayesian computation coupled with deep learning analyses

44 Using an approximate Bayesian computation method, we estimate the age of the

45 oceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagn

46 e precision by using trial-level model-based Bayesian computational modeling and probability analyses

47 social psychological constructs in terms of Bayesian computations and provides a generative testing

48 esian perceptual inference but how are these Bayesian computations instantiated neurally?

49 The Bayesian Cost-Effectiveness Analysis package and the She

50 ) value for Brazil was estimated at 3.1 (95% Bayesian credible interval = 2.4-5.5), with a higher med

51 igned to oseltamivir (hazard ratio 1.29, 95% Bayesian credible interval [BCrI] 1.20-1.39) overall and

52 O(2) from baseline to 24 weeks was 1.72 (95% Bayesian credible interval, 1.02-2.42) mL/kg per minute

53 Bayesian cue integration suggests that this uncertainty

54 uncertainty is one of the main signatures of Bayesian decision making.

55 Bayesian decision theory provides a simple formal elucid

56 A Bayesian decision tree was used to estimate the probabil

57 e experimental results are corroborated by a Bayesian decision-making model which tracked the partici

58 We used a Bayesian design with an informative prior, so the primar

59 mal patterns of reasoning; but what of "anti-Bayesian" effects where the mind updates in a direction

60 scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models.

61 ng longitudinal traits by coupling Empirical Bayesian Estimates from mixed-effects modeling with a no

62 Cmax and AUC) from individual-level post-hoc Bayesian estimates of plasma and intrapulmonary pharmaco

63 This is consistent with a Bayesian estimation framework where the motor system red

64 ompertz model, especially when combined with Bayesian estimation.

65 Bayesian evolutionary rate and divergence date estimates

66 We present a novel sparse Bayesian factor model to explore the network structure a

67 Bayesian factorization methods, including Coordinated Ge

68 ly partitioning posterior probabilities from Bayesian fine-mapping.

69 use hidden Markov models (HMMs) fitted in a Bayesian framework and hourly Global Positioning System

70 We introduce a Bayesian framework for information sharing across cells

71 The flexibility of a Bayesian framework is promising for GWAS, but current ap

72 Behavioral analyses using a Bayesian framework showed that animals inferred reversal

73 Network meta-analyses using a bayesian framework to derive risk ratios (RRs) and risk

74 Using a hierarchical Bayesian framework to fit a modified version of prospect

75 e high-efficiency provided by the multi-task Bayesian framework to integrate information from differe

76 Here we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin or

77 fects cumulative probability models within a Bayesian framework were used to estimate differences bet

78 fects cumulative probability models within a Bayesian framework were used to estimate the treatment e

79 Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty

80 ntary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-sc

81 We modelled heterogeneity in a Bayesian framework, taking overall mortality as a primar

82 ers, phylogeny, and nuisance parameters in a Bayesian framework.

83 lied the MixSIAR un-mixing modelling under a Bayesian framework.

84 ic and daily reproduction numbers, we used a Bayesian framework.

85 f unintended pregnancy and abortion within a Bayesian framework.

86 g under volatility task using a hierarchical Bayesian framework.

87 to identify associated biomarkers with a new Bayesian framework.

88 We developed two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) m

89 We introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estima

90 Bayesian genetic fine-mapping studies aim to identify th

91 tant vector control insecticides to inform a Bayesian geostatistical ensemble modelling approach, gen

92 d that the data were well accounted for by a Bayesian heuristic model, in which the agent continues s

93 ion, as opposed to using some manifestly non-Bayesian heuristic.

94 lowing methods: haplo.glm, hapassoc, HapReg, Bayesian hierarchical generalized linear model (BhGLM) a

95 itatively compare circRNA levels and (iii) a Bayesian hierarchical model for DE analysis of circRNAs

96 For statistical inference, a Bayesian hierarchical model is used to study the distrib

97 A Bayesian hierarchical model of heterogeneous, clustered

98 We used a Bayesian hierarchical model with country-specific time t

99 We describe a Bayesian hierarchical model, called Bayesian Inference o

100 bleaching and nitrogen availability within a Bayesian hierarchical modeling framework, we tested the

101 Using phylogenetic Bayesian hierarchical models and high-resolution satelli

102 ments of 531 mothers and pups were used with Bayesian hierarchical models to explain the relationship

103 Bayesian hierarchical multivariable regression with gene

104 Cognitive trajectories were modelled in a Bayesian hierarchical regression framework to estimate t

105 using publicly available death data within a Bayesian hierarchical semi-mechanistic framework.

106 Bayesian hierarchical spatio-temporal Poisson models wer

107 Bayesian hyper-parameter optimization confirmed our noti

108 image-computable (images in, estimates out) Bayesian ideal observer.

109 to assess the phylogenetic relationships by Bayesian inference (BI) and maximum likelihood (ML) sear

110 that NMDAR dysfunction impairs hierarchical Bayesian inference about the world's statistical structu

111 mathematical connections: dynamical systems, Bayesian inference and reinforcement learning.

112 trained using model simulations-to carry out Bayesian inference and retrieve the full space of parame

113 cent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conf

114 test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to

115 Advances in Bayesian inference methods for individual-level geo-loca

116 e evolution, based on parsimony analysis and Bayesian inference of a new morphological dataset.

117 BICORN (Bayesian Inference of COoperative Regulatory Network) bu

118 scribe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which inte

119 Maximum-likelihood and Bayesian inference phylogenetic analyses of 13 PCGs and

120 We evaluate the performance of our Bayesian inference procedure through extensive simulatio

121 s or motivated cognition, but recent work in Bayesian inference suggests that belief maintenance can

122 ombined with five clinical features by using Bayesian inference to develop probability-ranked differe

123 olecular genotyping with network science and Bayesian inference to infer directed genotype networks-a

124 listic predictive model and trained it using Bayesian inference with the longitudinal data from two p

125 We analysed these with maximum likelihood, Bayesian inference, and a multispecies coalescent summar

126 nsive phylogenetic analyses using parsimony, Bayesian inference, and maximum likelihood all support F

127 ter predicted by reinforcement learning than Bayesian inference, and that older adults rely more on r

128 nciple, which bridges information theory and Bayesian inference, we derive a maximum entropy model of

129 onstrue the brain as performing hierarchical Bayesian inference.

130 ariability (adjusted R(2) = 0.53; P < 0.001; Bayesian information criterion [BIC] decrease of 527; ch

131 candidates for multivariable modeling using Bayesian information criterion.

132 ch mediation may be compatible with proposed Bayesian information-processing principles.

133 Previous studies that proposed a Bayesian interpretation of perceptual organization, howe

134 Covariate-adjusted Bayesian kernel machine regression was used to investiga

135 Using Bayesian kernel machine regression, we found that higher

136 tistic for spatial clustering, and Empirical Bayesian Kriging.

137 eneralized linear model (BhGLM) and logistic Bayesian LASSO (LBL).

138 ding BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO.

139 Using Bayesian Latent Class Analysis, we estimated the sensiti

140 PheLEx consists of a hierarchical Bayesian latent variable model, where inference of diffe

141 Using a computational model of Bayesian learning under uncertainty in a reversal learni

142 ions between case groups were examined using Bayesian linear mixed effects models, which included par

143 Bayesian linear mixed effects regression models were con

144 Here, we compared Bayesian logistic and time-to-event approaches to modeli

145 Our results show that a Bayesian logistic model using full-information continuou

146 were used to evaluate future T2D risk using Bayesian logistic regression.

147 s of trust, were determined using univariate Bayesian logistic regressions.

148 Bayesian machine learning methods can be used for prospe

149 in vitro data to assign a bioactivity score, Bayesian machine learning methods can be used for prospe

150 ing maximum-likelihood trees and dated using Bayesian Markov Chain Monte Carlo inference.

151 oftware enhance the efficiency of the CoGAPS Bayesian matrix factorization algorithm so that it can a

152 to overcome the computational limitations of Bayesian matrix factorization for single cell data analy

153 ant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct a

154 Respective Bayesian mean covariate-adjusted pCR rates and percentag

155 We further analyse the dataset using a Bayesian measurement model, which shows the quick accele

156 k Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple

157 DecontX is a novel Bayesian method to estimate and remove contamination in

158 Here, we propose a fully Bayesian method to infer ensembles of chromatin structur

159 A significant contribution from the Bayesian method was realizing that the variability of th

160 sample validation suggests that the proposed Bayesian method, which incorporates physics-guidance, ha

161 [Formula: see text] is based on a sequential Bayesian method, while the parameter [Formula: see text]

162 mparative benchmarking with Bayesian and non-Bayesian methods (LSD, TreeTime, and treedater), we foun

163 tate-level policies from 1970 to 2016, using Bayesian methods and a modeling approach that addresses

164 ssigned in a response-adaptive manner, using Bayesian methods and stratified by age group (<18 years,

165 First, we use Bayesian methods to incorporate assumptions about the ba

166 by analyzing ~20,000 fossil occurrences with Bayesian methods to infer dispersal and diversification

167 variate analyses, clustering algorithms, and Bayesian methods, we found evidence for moderately low r

168 ing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach.

169 Bayesian mixing model results suggest that during the 19

170 groups and their relative nutrients using a Bayesian Mixing Model revealed distinct subsistence stra

171 Here, we develop a hierarchical Bayesian mixture model that describes this complex proce

172 The underlying Bayesian model accommodates phylogenetic structure in th

173 idity data from 3,694 sites worldwide with a Bayesian model and found that K(d) 490, a measurement po

174 ve Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on

175 wo-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-th

176 work model of the inferior olive and a novel Bayesian model averaging approach.

177 vity to outcome values, and the hierarchical Bayesian model captured some markers of transfer, only h

178 We perform a Bayesian model comparison for two constraint models and

179 Bayesian model comparison of various a priori hypotheses

180 an optimal observer model and an approximate Bayesian model in which participants were assumed to att

181 been mapped theoretically using a hierarchal Bayesian model of brain function that takes into account

182 tual distance, and was best captured using a Bayesian model of generalization that formalized distanc

183 We formalize a Bayesian model of hierarchy discovery that explains how

184 r software implements a probabilistic, fully Bayesian model of screen data.

185 We use a Bayesian model reduction approach that combines Parallel

186 Bayesian model selection indicates that Mec1 primarily u

187 ents, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive

188 We then use Bayesian model selection to recover the most likely unde

189 decision tasks could be best explained by a Bayesian model that combines reinforcement-based learnin

190 We developed a hierarchical Bayesian model to estimate population numbers in small a

191 Using a Bayesian model to estimate the probability of disease, t

192 Here we propose a Bayesian model to explore how violent and property crime

193 We applied a hierarchical Bayesian model to single-trial EEG data from healthy hum

194 A Bayesian model was employed to decompose resting-state f

195 feature uncertainty (i.e., entropy) and the Bayesian model were both strong predictors of these beha

196 ion time was calculated using an ex-Gaussian Bayesian model.

197 ucleoside transporter (ENT) 1 and ENT2 using Bayesian modeling.

198 by each learning system, using hierarchical Bayesian modeling.

199 ar-specific discard rates estimated within a Bayesian modelling framework.

200 lve the age of the Ngandong evidence, we use Bayesian modelling of 52 radiometric age estimates to es

201 Bayesian models generated using Assay Central machine le

202 Bayesian models of behavior suggest that organisms repre

203 havior and provide a neural underpinning for Bayesian models of perception.

204 efit from a reformulation that takes current Bayesian models of the brain into account.

205 Bayesian models showed that associations between eicosan

206 We fit hierarchical Bayesian models to these data to describe both the mean

207 th representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption proper

208 to avoid confounds and fitting hierarchical Bayesian models.

209 perative length of stay were evaluated using Bayesian models.

210 tric reasoning does not conform to normative Bayesian models: we saw no evidence for use of priors in

211 We used a Bayesian multinomial logit Gaussian process model to pro

212 meter values, then calibrated models using a Bayesian multiobjective optimization procedure, and sele

213 We used Bayesian multivariate response random effects logistic r

214 ne regulations, either by applying a dynamic Bayesian network (DBN) inference algorithm, genist, or a

215 presents an experimental demonstration of a Bayesian network building block implemented with inheren

216 We conducted a random-effects Bayesian network meta-analysis using standardized mean d

217 n representing the stochastic variables in a Bayesian network that encode the probability of occurren

218 simulation of an example case of a four node Bayesian network using our proposed device, with paramet

219 The accuracy of the Bayesian network was better than that of neuroradiology

220 It also implements a simple 2-node Bayesian network.

221 variables, the complexity of the associated Bayesian networks become computationally intractable.

222 t hardware, we demonstrate that any two node Bayesian networks can be implemented by our stochastic n

223 However, the few hardware implementations of Bayesian networks presented in literature rely on determ

224 ree samplers tested are good alternatives to Bayesian Networks since they are less computationally de

225 , still some modifications are needed in the Bayesian networks to be able to sample correctly the unc

226 Bayesian networks were estimated, to uncover complex int

227 west quartile at 72 hours was assessed using Bayesian networks.Measurements and Main Results: In the

228 Here we develop a hierarchical Bayesian non-parametric model of population growth that

229 This Bayesian omics-data fusion based methodology allows to g

230 , when the noise contrast modulates then the Bayesian optimal observer weights the template at each p

231 target region, then this TM observer is the Bayesian optimal observer.

232 ple deviate slightly but systematically from Bayesian optimality, while still performing "probabilist

233 r of infected farms during an outbreak using Bayesian optimisation and a simulation-based model of BT

234 rk demonstrates the potential value of using Bayesian optimisation in developing cost-effective disea

235 using data from the first few cycles, and a Bayesian optimization algorithm(10,11), which reduces th

236 Here, we formulate olfactory learning as a Bayesian optimization process, then map the learning rul

237 is automated experimentation platform with a Bayesian optimization, a self-driving laboratory is cons

238 We introduce such a method within the Bayesian paradigm and apply it to anonymized mobile call

239 Taken together, our results demonstrate that Bayesian parameter estimation combined with regularizati

240 (probability discounting) using hierarchical Bayesian parameter estimation.

241 and brain imaging, in-scanner kinematics and Bayesian pattern component modelling, we show that CoM-s

242 Human speech perception can be described as Bayesian perceptual inference but how are these Bayesian

243 sing state-of-the-art maximum-likelihood and Bayesian phylogenetic analyses.

244 to identify correlates of prevalent HCV, and Bayesian phylogenetic analysis was used to examine genet

245 rk analysis to identify 8 trophic guilds and Bayesian phylogenetic modeling to show that trophic guil

246 We used maximum-likelihood and Bayesian phylogenetics to analyse new (N = 163) and prev

247 integrate individual travel history data in Bayesian phylogeographic inference and apply it to the e

248 ptom duration, and symptom severity, using a Bayesian piece-wise exponential primary analysis model.

249 we formulate a well-posed hypothesis from a Bayesian point of view and suggest a nonparametric test

250 mpanied by estimates of uncertainty based on Bayesian posterior probabilities.

251 We propose using a Bayesian predictive approach, which enables researchers

252 f these signatures if and when it has an 85% Bayesian predictive probability of success in a hypothet

253 nomic covariates, and survey data informed a Bayesian prevalence model, in which a flexible incidence

254 matively, these beliefs should correspond to Bayesian probabilities.

255 nd persuasion and is typically modeled using Bayesian probability theory rather than logic.

256 ementation based on phenotype ontologies and Bayesian probability updates.

257 In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clust

258 Bayesian ranking techniques may offer a solution to this

259 pared to existing methodologies designed for Bayesian recovery of disease phenotypes.

260 omy assignment disambiguation with empirical Bayesian redistribution.

261 te the probability of clinical benefit using Bayesian regression models (an optimistic prior for the

262 Bayesian regression models were fitted to survival outco

263 Five Bayesian regression models were studied for classificati

264 round in brain shape and contrast, we used a Bayesian regression technique, Gaussian process regressi

265 These features were used to train a Bayesian regularized artificial neural network (BRANN) m

266 on study), and estimated effect size using a Bayesian ridge regression (BRR) model.

267 able experimental space, driven by a batched Bayesian search algorithm(16-18).

268 The variance is estimated by the Bayesian shrinkage approach to fully exploit the informa

269 area of the gastropod Tritia neritea, using Bayesian skyline plots (BSP), multivariate analyses and

270 nt a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothing) capable of producing smooth and accu

271 e develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM).

272 performs other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process

273 Bayesian spatial generalized linear mixed model whole-br

274 2017) in the contiguous USA and formulated a Bayesian spatio-temporal model to quantify how anomalous

275 en updated using satellite observations in a Bayesian spatio-temporal model.

276 estimates from short series by using recent Bayesian spectral fusion methods.

277 Bayesian stable isotope mixing models fitted with a TDF(

278 s, we included robust frequentist as well as Bayesian statistical analyses.

279 Here, we present a Bayesian statistical approach that uses latent space mod

280 Here we use a Bayesian statistical framework and a large sequence data

281 In this work, we develop a novel Bayesian statistical framework for inferring natural sel

282 d computation and, making use of approximate Bayesian statistical inference, with experimental measur

283 We complemented these analyses with Bayesian statistics and found no evidence in favor of th

284 Bayesian statistics revealed no interaction between pred

285 advances in state-trace analysis make use of Bayesian statistics to quantify the evidence for and aga

286 We fit linear Ricker growth models using Bayesian statistics to seven time series of elk populati

287 behavior accordingly, in a way predicted by Bayesian statistics.

288 ping with whole-genome sequencing data using Bayesian statistics.

289 n, southcentral Alaska, using a hierarchical Bayesian stock-recruitment model.

290 ve performance, they fell short of the ideal Bayesian strategy.

291 We develop a Bayesian structural equation modeling coupled with linea

292 The counterfactual was estimated using a Bayesian structural time-series model based on mortality

293 Bayesian structural time-series modeling is a promising

294 ng five different classifiers: probabilistic bayesian, Support Vector Machines (SVM), deep neural net

295 -slope models becomes feasible with standard Bayesian techniques, or with frequentist methods that al

296 tive process play a key role in hierarchical Bayesian theories of schizophrenia.

297 re promising avenue for the development of a Bayesian theory of culture.

298 challenges, we present MixEHR, a multi-view Bayesian topic model.

299 The Bayesian trial design enables outcome data from open coh

300 Our BGW-TWAS method is based on Bayesian variable selection regression, which not only a