ライフサイエンスコーパス: Bayesian

1 Over the 3-year period, 31.9% (Bayesian), 23.4% (old-CMS), and 19.8% (new-CMS) of cente

2 RR-BLUP, Bayesian A, B, Cpi, LASSO, Ridge Regression and two mach

3 oring System, were randomly assigned using a Bayesian adaptive design to receive either azacitidine 7

4 ineated on PET with a fuzzy locally adaptive bayesian algorithm.

5 Bayesian analyses revealed that hypervigilant responding

6 Maximum likelihood and Bayesian analyses yield robust phylogenetic trees.

7 Maximum-likelihood and Bayesian analyses yielded similar tree topologies that w

8 lear sequence data using neutrality test and Bayesian analysis (EBSP and ABC).

9 These examples illustrate how Bayesian analysis integrates new trial information with

10 Bayesian analysis is firmly grounded in the science of p

11 imaquine-induced haemolysis using a holistic Bayesian analysis of all published data and was used to

12 propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estima

13 A Bayesian analysis of intraspecific taxon ages indicates

14 programming code and data sets, to show how Bayesian analysis takes evidence from randomized clinica

15 Bayesian analysis using a neutral prior indicated a 76%

16 r regression (LR), linear mixed model (LMM), Bayesian analysis with spike-slab priors (Bayes B) and l

17 e polymer everolimus-eluting stent was 100% (Bayesian analysis, difference in target lesion failure f

18 in the surgery group (95% credible interval [Bayesian analysis] for difference, -5.2 to 2.3%; posteri

19 We used Bayesian analytical methods (with a margin of 0.07) to e

20 the combination of two classifiers, a naive Bayesian and a random forest classifier, through a votin

21 rence of structural alignments, built on the Bayesian and information-theoretic principle of Minimum

22 l learning task evaluating performance using Bayesian and Reinforcement learning models.

23 true annotation from random annotation with Bayesian annotation probability >0.95.

24 This Bayesian approach allowed us to determine strategies for

25 the progress curve assay, here we propose a Bayesian approach based on an equation derived with the

26 By using an approximate Bayesian approach employing distribution of fragment len

27 We developed a new Bayesian approach for identifying the loci underlying an

28 This paper gives the first Bayesian approach for testing Hardy-Weinberg equilibrium

29 n framework, with a better robustness of the Bayesian approach for the sparsest data sets.

30 d processes in order to draw inferences, our Bayesian approach includes the unobserved infection time

31 This novel non-parametric Bayesian approach is demonstrated on a variety of data s

32 We address this issue by proposing a Bayesian approach that accounts for age uncertainty inhe

33 ssion parameters jointly using a constrained Bayesian approach that ensures that one remains within t

34 , East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in alleli

35 ransversal algorithm to detect AS; anchor, a Bayesian approach to assign modalities; and bonvoyage, a

36 We employ a Bayesian approach to estimate the posterior distribution

37 The primary analysis used a Bayesian approach to evaluate the hypothesis that the pr

38 finable parameters, relying on a model-based Bayesian approach which takes full account of the indivi

39 Our flexible Bayesian approach will be especially useful to improve c

40 d using hierarchical models and an empirical bayesian approach with volume-based shrinkage that allow

41 ckage allows one to infer parameters using a Bayesian approach, perform forward modelling of the like

42 Taking a Bayesian approach, we quantify the trade off between dif

43 Using a Bayesian approach, we show that Gaussian processes model

44 Using a Bayesian approach, we show that, in contrast to earlier

45 anobacterial and chloroplast genomes using a Bayesian approach, with the aim of estimating the age fo

46 owth between birth and 1 year of age using a Bayesian approach.

47 tal registration system (1997-2014), using a Bayesian approach.

48 sing standard frequentist and random-effects Bayesian approaches.

49 Bayesian association analysis suggests that BMI is highl

50 A Bayesian association scan, informed by these priors, for

51 We developed a Bayesian-based method for genome-wide association studie

52 on and persistence of DRM was assessed using Bayesian-based statistical modeling.

53 To compare the utility of this Bayesian basket (BB) design with that of a balanced rand

54 that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM

55 sensitivities for detecting influenza A from Bayesian bivariate random-effects models were 54.4% (95%

56 This bound is called Fast Bayesian Bound (FBB) and serves as a canonical reference

57 ms are provided to efficiently calculate the Bayesian bound that under some conditions becomes the eq

58 he Ti2AlC-Cr2AlC via first-principles-guided Bayesian CALPHAD framework that accounts for uncertainti

59 gration and spatial, temporal prediction and Bayesian causal inference.SIGNIFICANCE STATEMENT Looming

60 igante rockshelter (Honduras) to establish a Bayesian chronology over the past approximately 11,000 y

61 The salient problem confronting optimal Bayesian classification is prior construction.

62 Optimal Bayesian classification provides optimal classification

63 Moreover, the extension of optimal Bayesian classification to multinomial mixtures where da

64 With optimal Bayesian classification, uncertainty is treated directly

65 Here, the authors take an in silico naive Bayesian classifier approach to integrate multiple lines

66 s were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in t

67 ication strategy combining a one-dimensional Bayesian classifier with a support vector machine.

68 Based on Bayesian clustering analysis and hybridization simulatio

69 n effective population size (Ne ) and used a Bayesian-coalescent based approach that simultaneously c

70 We use conditional analysis and a Bayesian colocalisation method to provide evidence of a

71 A Bayesian comorbidity network was constructed to model th

72 astly, we use the BSMC within an Approximate Bayesian Computation (ABC) inference scheme, and suggest

73 icit mathematical models with an approximate Bayesian computation approach can be used to assign the

74 ts current global range using an Approximate Bayesian Computation approach.

75 evolutionary scenarios under the approximate Bayesian computation framework and estimate that the spe

76 Furthermore, approximate Bayesian computation provides a robust method for visual

77 Approximate Bayesian computation suggested that the most likely spec

78 ontal cortex's activity is consistent with a Bayesian computation that integrates social information

79 rk based on network analysis and Approximate Bayesian Computation to quantify host shift and cospecia

80 We used approximate Bayesian computation to test five different demographic

81 from linkage disequilibrium and approximate Bayesian computation were approximately 50 and 30, respe

82 SpartaABC implements an approximate-Bayesian-computation rejection algorithm to infer indel

83 A novel Bayesian computational model revealed that subjective sa

84 sis (in the analysis of spatial biases), and Bayesian computational modeling (in the analysis of indi

85 that a generalized magnitude system based on Bayesian computations would minimally necessitate multip

86 shed findings, could be reproduced by BACON (Bayesian Context Fear Algorithm), a physiologically real

87 In Bayesian cost-effectiveness analyses, likelihood that CP

88 We used Bayesian Cox regression to estimate reinfection rates ac

89 R) for achieving MRD negativity is 0.23 (95% Bayesian credible interval [BCI] 0.18-0.28) for pediatri

90 Bayesian criteria have significantly higher flagging rat

91 We calibrated LF transmission models using Bayesian data-model assimilation techniques to baseline

92 A Bayesian dated phylogeny, based on the 13 mitochondrial

93 To apply Bayesian decision analysis (BDA) to cancer therapeutics

94 Bayesian decision analysis is a systematic, objective, t

95 Our novel use of Bayesian demographic modelling shows that native insect

96 tems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematica

97 Bayesian distributed lag interaction models identified a

98 We used Bayesian distributed lag interaction models to identify

99 We implemented Bayesian-distributed lag interaction models to identify

100 ng variants (PTVs), we provide corresponding Bayesian estimates for individual genes.

101 Bayesian estimation of past effective population sizes r

102 rted to the quantum domain and combined with Bayesian estimation tools to experimentally reconstruct

103 orward to implement in standard software for Bayesian estimation.

104 More precisely, we show theoretically that a Bayesian estimator should reduce the weight of sensory i

105 initial value and confidence judgments in a Bayesian fashion, taking into account both the uncertain

106 ve models of human cognition often appeal to Bayesian filtering, which provides optimal online estima

107 Static fuzzy locally adaptive Bayesian (FLAB) volumes corresponded best with pathology

108 We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Composi

109 We present a Bayesian framework accounting for these features directl

110 present the evolution of sequence data in a Bayesian framework and the approximation of the posterio

111 CGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate statistical mod

112 We tested whether this basic Bayesian framework could explain human subjects' behavio

113 A Bayesian framework enabled probability statements for me

114 s trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various

115 n is achieved using metainference, a general Bayesian framework that accounts for both noise in the d

116 g vehicle and powertrain parameters within a Bayesian framework to determine the impact of engineerin

117 We sought to describe how a Bayesian framework using prior information from adult tr

118 In a Bayesian framework, perceptual estimates from sensory in

119 f the model, both in the frequentist and the Bayesian framework, with a better robustness of the Baye

120 the mode of inheritance can be inferred in a Bayesian framework.

121 ethylation at each locus within an empirical Bayesian framework.

122 pidemiological and programmatic data using a Bayesian framework.

123 We then developed a Bayesian Gaussian Regression model to measure the relati

124 -qPCR validations, eXpress and Multi-Mapping Bayesian Gene eXpression (MMBGX) programs achieved the b

125 We used a Bayesian geostatistical analytical framework to generate

126 A Bayesian hierarchical approach for individual patient da

127 We propose new Bayesian hierarchical Cox proportional hazards models, c

128 applied deep-sequencing techniques within a Bayesian hierarchical framework to a cohort of 24 transm

129 stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation

130 lipid levels in 828 Hutterites and applied a Bayesian hierarchical framework to prioritize potentiall

131 e rate and required less computing time than Bayesian hierarchical generalized linear model, efficien

132 A Bayesian hierarchical logistic regression model was appl

133 emperature, and Nino3.4 index forecasts in a Bayesian hierarchical mixed model to predict dengue inci

134 tors affecting safety as covariates within a Bayesian hierarchical model to estimate the global, regi

135 s to generate country-specific results using Bayesian hierarchical modeling.

136 Bayesian hierarchical models were used to quantify assoc

137 urvival rates, and volume of LP centers with Bayesian, historical (old-CMS) and new-CMS criteria usin

138 ation Analysis (AMA) is a recently developed Bayesian ideal observer method for task-specific dimensi

139 We derive a fast variational Bayesian inference algorithm and show that it correctly

140 l pooling, next-generation sequencing, and a Bayesian inference algorithm to rapidly process and then

141 ckage performing such accurate and efficient Bayesian inference for enzyme kinetics is provided.

142 unity structure, and develop a nonparametric Bayesian inference framework that identifies the simples

143 We outline a Bayesian inference model, incorporating the key componen

144 Bayesian inference of evolutionary rates shows that geno

145 As our approach relies on Bayesian inference our scheme transcends individual sequ

146 By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promo

147 ir respective reliability, as predicted by a Bayesian inference scheme.

148 within the context of current notions about Bayesian inference that find their historical roots in v

149 depositions to BioMagResBank, and tools for Bayesian inference to enhance the robustness and extensi

150 the early olfactory system uses approximate Bayesian inference to solve it.

151 We use Bayesian inference with MCMC for which we have designed

152 n index based on stochastic block models and Bayesian inference) of each articulation.

153 mitochondrial and two nuclear markers, using Bayesian Inference, Maximum Likelihood, genetic divergen

154 ly, evoked neural activities were modeled by Bayesian inference, which had a top-down explaining-away

155 t directly mimics the computational units of Bayesian inference.

156 We describe a Bayesian integrated approach we developed that combined

157 core of the yeast spindle pole body (SPB) by Bayesian integrative structure modeling based on in vivo

158 We employed a novel statistical approach, Bayesian kernel machine regression (BKMR), to study the

159 A Bayesian latent class model was constructed on the basis

160 We compared performance to the ideal Bayesian learner and several suboptimal models that vari

161 k is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty assoc

162 Additionally, Bayesian LMMs allow for more flexible assumptions about

163 Using three recently published Bayesian LMMs, Bayes R, BSLMM, and BOLT-LMM, we investig

164 A Bayesian machine learning technique, called Graphical Mo

165 Bayesian markets transform this correlation into a mecha

166 per shows how to design such markets, called Bayesian markets.

167 We implemented the Bayesian maximum entropy approach to blend data with unc

168 aximum likelihood approaches are NP-hard and Bayesian MCMC methods do not scale well to even moderate

169 By using Bayesian meta-analysis, we found moderate evidence of a

170 A Bayesian method (BPEC) was used to detect four geographi

171 Our fully Bayesian method couples the iHMM to a continuous control

172 We present a hierarchical Bayesian method for identifying genetic interactions thr

173 A Bayesian method identified the largest p-value threshold

174 It differs from classical Bayesian methods in which a classification model is assu

175 genetic analyses, Statistical Parsimony, and Bayesian methods were used to infer genetic diversity, g

176 Bayesian methods were used to test for mediation.

177 eotide datasets using maximum likelihood and Bayesian methods, comprising all 13 mitogenomes currentl

178 Using direct overlap and Bayesian methods, we identified new potential target gen

179 urable polymer everolimus-eluting stent with Bayesian methods.

180 Bayesian mixed models estimated the plausible range of e

181 We used a Bayesian mixed-effects model to account for between-stud

182 We used Bayesian mixed-effects regression models to estimate mor

183 multimodel ensemble mean (MME), we used the Bayesian model averaging (BMA) to optimize the integrati

184 Bayesian model averaging was used to determine parameter

185 thod for reconciling these differences using Bayesian model averaging.

186 Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate th

187 We present a novel hierarchical Bayesian model called Differentially Expressed Isoform d

188 Bayesian model comparison demonstrates that the SERIA mo

189 ociated with rare hereditary disorders using Bayesian model comparison.

190 Here, we present a Bayesian model for exploratory data analysis that is cap

191 We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on

192 A hierarchical Bayesian model is developed to fully take into account t

193 A hierarchical Bayesian model is employed to assess uncertainty on a ge

194 A hierarchical Bayesian model of learning suggested that in ASD, a tend

195 e formulation of this loop as a hierarchical Bayesian model points to key computational quantities th

196 Bayesian model selection between exponential and gamma d

197 According to Bayesian model selection, the best account for medicatio

198 ethod that overcomes these problems based on Bayesian model selection.

199 e, we describe a national-scale hierarchical Bayesian model that addresses these issues and that pred

200 We also present a Bayesian model that combines intrinsic enzymatic specifi

201 ve populations, and developed a hierarchical Bayesian model to examine the relationship between CWD p

202 variational EM algorithm for a hierarchical Bayesian model to identify rare variants in heterogeneou

203 al. resolve this issue using a pan-Antarctic Bayesian model to infer missing data, and show that spat

204 nference and forecasts from our hierarchical Bayesian model to phenomenological regression-based meth

205 Here we develop a novel Bayesian model to simultaneously estimate correlations b

206 It implements a Bayesian model using strain-typed surveillance data from

207 Here, we describe a Bayesian modeling approach to the antisaccade task that

208 y distinct Cd end-members was confirmed by a Bayesian modeling approach.

209 tmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge re

210 quence in specifying where GR binds, we used Bayesian modeling within the universe of accessible chro

211 By using Bayesian modeling, we show that there is no difference a

212 iewpoint compatible both with non-parametric Bayesian modelling and with sub-symbolic methods such as

213 ion of recent statistical theory in a sparse Bayesian modelling framework.

214 Hierarchical Bayesian modelling provides a framework to make statisti

215 To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implement

216 Bayesian models propose that multisensory integration de

217 ality in each city, and we used hierarchical Bayesian models to combine the city-specific estimates.

218 METHOD: Bayesian models were developed using a retrospective coh

219 We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gen

220 Importantly, the Bayesian nature of the model allows one to "seed" decode

221 a recently developed analysis framework for Bayesian network (BN) modeling to analyze publicly avail

222 Bayesian network analysis revealed key driver genes with

223 We did a Bayesian network meta-analysis to produce incidence rate

224 We performed a Bayesian network meta-analysis using a fixed-effect mode

225 e conducted random-effects meta-analysis and Bayesian network meta-analysis.

226 st-operative stroke rate, were included in a Bayesian network meta-analysis.

227 Through simulation of a reverse-engineered Bayesian network model, we generated predictions of G1-S

228 l trial simulation framework using iterative Bayesian network modeling and a pharmacokinetic-pharmaco

229 ing a systems science approach, we performed Bayesian network modeling to find the most accurate repr

230 tic regression, naive Bayes classifier and a Bayesian network using noisy OR gates.

231 By integrating the Bayesian network with logistic regression, current produ

232 rithms, specifically, a tree-augmented naive Bayesian network, a random forest algorithm, and a gradi

233 integration, composite association network, Bayesian network, semi-definite programming-support vect

234 ring data were analyzed using regression and Bayesian networks (BNs) to explore factors influencing t

235 Predictive functional dynamics and Bayesian networks implied that the taxa putatively not c

236 ried the transcriptomes and inferred dynamic Bayesian networks of gene expression across early leaf o

237 In this paper we use Bayesian networks to determine and visualise the interac

238 at combines species distribution models with Bayesian networks, which enables the direct and indirect

239 Bayesian NMAs were performed to combine direct compariso

240 ts (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS.

241 es of cue predictability were derived from a Bayesian observer model of behavioral responses.

242 estimates mean and variability with a Basic Bayesian observer model, the estimate distributions were

243 ed the data than the Basic and several other Bayesian observers.

244 Bayesian, old-CMS and new-CMS criteria identified 13.4%,

245 Our data suggest that humans can approximate Bayesian optimality with a switching heuristic that forg

246 We perform an extensive comparison of Bayesian optimized deep survival models and other state

247 descriptive statistics and supplemented with Bayesian ordinal model-based estimation.

248 kers with grade 3 PGD was analyzed under the Bayesian paradigm, using logistic model and areas under

249 It then uses a Bayesian partition model to simultaneously partition the

250 l approach to tackle multiple testing from a Bayesian perspective through posterior predictive checks

251 We used maximum entropy and Bayesian phylodynamic models to generate risk maps for P

252 Additionally, Bayesian phylogenetic analyses corroborate several linea

253 demiological contact tracing of patients and Bayesian phylogenetic analysis of bacterial WGS data wer

254 used open source software package to perform Bayesian phylogenetic inference.

255 We apply Bayesian phylogenetic reconstruction to infer the time p

256 A Bayesian phylogenetic tree, together with associated dat

257 We employed a Bayesian phylogeography approach to characterize the eme

258 Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been imp

259 red error reconstruction techniques based on Bayesian point process (Snyder) filters.

260 To address this, we fit a Bayesian population dynamics model that includes process

261 nment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of

262 sh uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters a

263 t neighbors, local least squares regression, Bayesian principal components analysis, singular value d

264 d based upon previous observations following Bayesian principles, but little is known about the under

265 ue attribution and choice based on normative Bayesian principles.

266 at our new method for the computation of the Bayesian prior on node ages reduces the running time for

267 been fruitfully characterized as a normative Bayesian process in which sensory evidence and priors ar

268 We then used a Bayesian process to calibrate the model to clinical data

269 Bayesian Quantal Analysis (BQA) of evoked EPSCs showed t

270 We pooled serotype-specific estimates using Bayesian random-effects models.

271 METHODS AND We performed a Bayesian random-effects network meta-analysis of 124 tri

272 of these, we identify credible SNPs using a Bayesian refinement approach, with two loci harbouring o

273 We develop a Bayesian semiparametric model, which combines low-rank f

274 The results of this paper are based on a Bayesian setup in which people use their private informa

275 A Bayesian skyline plot revealed the rapid expansion of CR

276 Here, we present a novel Bayesian smoothing approach (called ABBA) to detect diff

277 We present a fast Bayesian sparse factor model, which takes input gene exp

278 methods (synthetic controls and hierarchical Bayesian spatial regression) to test whether the decline

279 Three Bayesian spatiotemporal conditional autoregressive model

280 Bayesian state-space and habitat models show that pengui

281 mbining a multispecies Gompertz model with a Bayesian state-space framework, we quantify community-le

282 We developed a flexible Bayesian statistical approach to quantify allele-specifi

283 and the Big Five personality traits using a Bayesian statistical framework.

284 HCV4a and HCV4d evolutionary histories in a Bayesian statistical framework.

285 Using the latest advances in Bayesian statistical inference for intractable models, w

286 NA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression

287 evolution of these disharmonies by means of Bayesian statistics.

288 pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) fr

289 We used Bayesian survival trajectory analysis to study age-speci

290 We expanded a previously published Bayesian synthesis framework to account for all vaccine

291 habitats by developing a multi-trophic level Bayesian three-isotope mixing model.

292 We use a Bayesian time series regression to estimate the long-ter

293 mutational signatures based on an empirical Bayesian treatment of the NMF model.

294 hange-point detection is proposed by using a Bayesian-type statistic based on the shortest Hamiltonia

295 s applied to approximate the significance of Bayesian-type statistics.

296 the repulsion exponent through Hierarchical Bayesian uncertainty quantification We use experimental

297 We introduce a Bayesian Unidimensional Scaling (BUDS) technique which e

298 Hence, we develop a flexible Bayesian variable selection model with efficient computa

299 nal LP evaluations for 1-year mortality with Bayesian versus new-CMS criteria with median differences

300 ns (52 vs 13 PSRs) for 1-year mortality with Bayesian versus new-CMS criteria.