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1Bayesian regression predictions - Tutorials - Pyro Discussion Forum
https://forum.pyro.ai/t/bayesian-regression-predictions/1115/8
4 лет назад , @becker
pyro
bayesian
bayes
regression
prediction
sample
pyrobayesianbayesregressionpredictionsample
копироватьудалить
- Запись сообщества
- посмотреть историю записи
1Remove dependency on TracePosterior and deprecate utilities in AbstractInfer · Issue #1930 · pyro-ppl/pyro
https://github.com/pyro-ppl/pyro/issues/1930
4 лет назад , @becker
pyro
bayesian
bayes
regression
prediction
sample
pyrobayesianbayesregressionpredictionsample
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- Запись сообщества
- посмотреть историю записи
1Financial forecasting with probabilistic programming and Pyro
https://medium.com/@alexrachnog/financial-forecasting-with-probabilistic-programming-and-pyro-db68ab1a1dba
4 лет назад , @becker
pyro
bayesian
bayes
regression
prediction
sample
pyrobayesianbayesregressionpredictionsample
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- Запись сообщества
- посмотреть историю записи
1machine learning - Bias and variance in leave-one-out vs K-fold cross validation - Cross Validated
https://stats.stackexchange.com/questions/61783/bias-and-variance-in-leave-one-out-vs-k-fold-cross-validation
6 лет назад , @becker
fold
classfier
variance
leaveoneout
one
bias
evaluation
leave
kfold
prediction
out
classification
foldclassfiervarianceleaveoneoutonebiasevaluationleavekfoldpredictionoutclassification
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1predictive models - Differences between cross validation and bootstrapping to estimate the prediction error - Cross Validated
https://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio
6 лет назад , @becker
strapping
evaluation
cross
kfold
prediction
bootstrapping
boot
classification
validation
strappingevaluationcrosskfoldpredictionbootstrappingbootclassificationvalidation
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- Запись сообщества
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1Early prediction and longitudinal modeling of preeclampsia from multiomics
I. Marić, K. Contrepois, M. Moufarrej, I. Stelzer, D. Feyaerts, X. Han, A. Tang, N. Stanley, R. Wong, G. Traber и 1 other автор(ы). Patterns, 3 (12): 100655 (2022)
2 лет назад , @becker
myown
modeling
omics
pregnancy
multiomics
preeclampsia
prediction
multi
longitudinal
myownmodelingomicspregnancymultiomicspreeclampsiapredictionmultilongitudinal
копироватьудалитьдобавить публикацию в буфер
1Predicting cell populations in single cell mass cytometry data
T. Abdelaal, V. van Unen, T. Höllt, F. Koning, M. Reinders, и A. Mahfouz. Cytometry Part A, 95 (7): 769--781 (2019)
2 лет назад , @becker
cytometry
mass
citedby:scholar:count:35
prediction
yuqi
citedby:scholar:timestamp:2022-11-22
single
cell
type
cytof
cytometrymasscitedby:scholar:count:35predictionyuqicitedby:scholar:timestamp:2022-11-22singlecelltypecytof
копироватьудалитьдобавить публикацию в буфер
2BoostEMM-Transparent Boosting using Exceptional Model Mining.
S. van der Zon, O. Zeev-Ben-Mordehai, T. Vrijdag, W. van Ipenburg, W. Duivesteijn, J. Veldsink, и M. Pechenizkiy. MIDAS@ PKDD/ECML, стр. 5--16. (2017)
3 лет назад , @becker
subgroup
exceptional
mining
emm
boosting
discovery
prediction
model
boost
classification
subgroupexceptionalminingemmboostingdiscoverypredictionmodelboostclassification
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1VoPo leverages cellular heterogeneity for predictive modeling of single-cell data
N. Stanley, I. Stelzer, A. Tsai, R. Fallahzadeh, E. Ganio, M. Becker, T. Phongpreecha, H. Nassar, S. Ghaemi, I. Maric и 20 other автор(ы). Nature Communications, 11 (1): 3738-- (2020)
4 лет назад , @becker
cytometry
mass
project:bmbf
myown
projectrelated
auto
predictive
heterogeneous
cell
p21
clustering
meta
gating
prediction
single
cytof
cytometrymassproject:bmbfmyownprojectrelatedautopredictiveheterogeneouscellp21clusteringmetagatingpredictionsinglecytof
копироватьудалитьдобавить публикацию в буфер
1Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy
J. Sugawara, D. Ochi, R. Yamashita, T. Yamauchi, D. Saigusa, M. Wagata, T. Obara, M. Ishikuro, Y. Tsunemoto, Y. Harada и 1 other автор(ы). BMJ open, 9 (2): e025939 (2019)
4 лет назад , @becker
log
monitoring
activity
complications
pregnancy
data
analysis
fetal
citedby:scholar:count:4
maternity
multiomics
prediction
citedby:scholar:timestamp:2020-8-11
lifelog
outcome
longitudinal
logmonitoringactivitycomplicationspregnancydataanalysisfetalcitedby:scholar:count:4maternitymultiomicspredictioncitedby:scholar:timestamp:2020-8-11lifelogoutcomelongitudinal
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2Deep learning for inferring gene relationships from single-cell expression data
Y. Yuan, и Z. Bar-Joseph. Proceedings of the National Academy of Sciences, 116 (52): 27151--27158 (2019)
4 лет назад , @becker
cnn
gene
deep
neural
prediction
learning
network
nn
single
cell
cnngenedeepneuralpredictionlearningnetworknnsinglecell
копироватьудалитьдобавить публикацию в буфер
11MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images
M. Steininger, K. Kobs, A. Zehe, F. Lautenschlager, M. Becker, и A. Hotho. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6 (3): 1--24 (2020)
4 лет назад , @becker
image
project:bmbf
mapping
myown
cnn
neural
air
network
networks
quality
p21
street
prediction
map
open
imageproject:bmbfmappingmyowncnnneuralairnetworknetworksqualityp21streetpredictionmapopen
копироватьудалитьдобавить публикацию в буфер
1Differential dynamics of the maternal immune system in healthy pregnancy and preeclampsia
X. Han, M. Ghaemi, K. Ando, L. Peterson, E. Ganio, A. Tsai, D. Gaudilliere, J. Einhaus, B. Bertrand, N. Stanley и 1 other автор(ы). Frontiers in immunology, (2019)
5 лет назад , @becker
nalab
immune
system
disruption
pree
correlation
paper:cordis
preeclampsia
maternal
prediction
iti
paper:fastcor
nalabimmunesystemdisruptionpreecorrelationpaper:cordispreeclampsiamaternalpredictionitipaper:fastcor
копироватьудалитьдобавить публикацию в буфер
1ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI
S. Fazli, M. Danóczy, J. Schelldorfer, и K. Mueller. NeuroImage, (2011)
5 лет назад , @becker
nalab
subgroups
effect
prediction
mixed
model
nalabsubgroupseffectpredictionmixedmodel
копироватьудалитьдобавить публикацию в буфер
2DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays
A. Singh, C. Shannon, B. Gautier, F. Rohart, M. Vacher, S. Tebbutt, и K. Lê Cao. Bioinformatics, (2019)
5 лет назад , @becker
immune
system
omics
mchri
bio
multiomics
prediction
preterm
multi
integration
immunesystemomicsmchribiomultiomicspredictionpretermmultiintegration
копироватьудалитьдобавить публикацию в буфер
5Understanding Where Your Classifier Does (Not) Work--The SCaPE Model Class for EMM
W. Duivesteijn, и J. Thaele. 2014 IEEE International Conference on Data Mining, стр. 809--814. IEEE, (2014)
5 лет назад , @becker
wouter
project:bmbf
subgroup
subgroups
mining
work
pattern
discovery
classifier
prediction
understand
classification
wouterproject:bmbfsubgroupsubgroupsminingworkpatterndiscoveryclassifierpredictionunderstandclassification
копироватьудалитьдобавить публикацию в буфер
2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China
L. Hu, J. Liu, и Z. He. Sustainability, 8 (8): 786 (2016)
7 лет назад , @becker
linear
use
air
osm
lur
quality
eva
pollution
citedby:scholar:count:4
regression
street
land
prediction
citedby:scholar:timestamp:2018-2-21
map
p2map
open
linearuseairosmlurqualityevapollutioncitedby:scholar:count:4regressionstreetlandpredictioncitedby:scholar:timestamp:2018-2-21mapp2mapopen
копироватьудалитьдобавить публикацию в буфер
6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, и A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628-640 (2015)PMID: 25947321.
7 лет назад , @becker
use
roads
air
lur
osm
quality
eva
pollution
features
regression
street
land
prediction
map
p2map
open
useroadsairlurosmqualityevapollutionfeaturesregressionstreetlandpredictionmapp2mapopen
копироватьудалитьдобавить публикацию в буфер
3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING
M. Adams. (2015)
7 лет назад , @becker
neural
use
air
lur
networks
quality
eva
pollution
regression
land
prediction
p2map
nn
neuraluseairlurnetworksqualityevapollutionregressionlandpredictionp2mapnn
копироватьудалитьдобавить публикацию в буфер
2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models
M. Adams, и P. Kanaroglou. Journal of environmental management, (2016)
7 лет назад , @becker
citedby:scholar:count:31
neural
use
air
lur
mobile
network
quality
eva
pollution
stationary
regression
land
prediction
citedby:scholar:timestamp:2018-2-21
p2map
nn
citedby:scholar:count:31neuraluseairlurmobilenetworkqualityevapollutionstationaryregressionlandpredictioncitedby:scholar:timestamp:2018-2-21p2mapnn
копироватьудалитьдобавить публикацию в буфер
14Supervised random walks: predicting and recommending links in social networks
L. Backstrom, и J. Leskovec. Proceedings of the fourth ACM international conference on Web search and data mining, стр. 635--644. ACM, (2011)
7 лет назад , @thoni
random
citedby:scholar:timestamp:2017-12-23
prediction
link
citedby:scholar:count:731
walk
randomcitedby:scholar:timestamp:2017-12-23predictionlinkcitedby:scholar:count:731walk
копироватьудалитьдобавить публикацию в буфер
2b+ WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring—Preliminary results and analysis
F. Murphy, M. Magno, P. Whelan, и E. Vici. Sensors Applications Symposium (SAS), 2015 IEEE, стр. 1--6. IEEE, (2015)
7 лет назад , @becker
bee
hobos
citedby:scholar:count:11
honey
machine
prediction
citedby:scholar:timestamp:2017-7-20
learning
bees
ml
weather
beehoboscitedby:scholar:count:11honeymachinepredictioncitedby:scholar:timestamp:2017-7-20learningbeesmlweather
копироватьудалитьдобавить публикацию в буфер
1b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring
F. Edwards-Murphy, M. Magno, P. Whelan, J. O’Halloran, и E. Popovici. Computers and Electronics in Agriculture, (2016)
7 лет назад , @becker
bee
hobos
citedby:scholar:count:4
honey
machine
prediction
citedby:scholar:timestamp:2017-7-20
learning
bees
ml
weather
beehoboscitedby:scholar:count:4honeymachinepredictioncitedby:scholar:timestamp:2017-7-20learningbeesmlweather
копироватьудалитьдобавить публикацию в буфер
2Using markov chains for link prediction in adaptive web sites
J. Zhu, J. Hong, и J. Hughes. Soft-Ware 2002: Computing in an Imperfect World, Springer, (2002)
7 лет назад , @becker
citedby:scholar:timestamp:2017-7-17
inthesis
prediction
citedby:scholar:count:174
link
diss
citedby:scholar:timestamp:2017-7-17inthesispredictioncitedby:scholar:count:174linkdiss
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1To explain or to predict?
G. Shmueli, и others. Statistical Science, 25 (3): 289--310 (2010)
7 лет назад , @becker
explain
citedby:scholar:count:513
inthesis
data
citedby:scholar:timestamp:2017-7-10
predict
prediction
diss
analysis
explanation
explaincitedby:scholar:count:513inthesisdatacitedby:scholar:timestamp:2017-7-10predictpredictiondissanalysisexplanation
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1Bayesian regression predictions - Tutorials - Pyro Discussion Forum

1Remove dependency on TracePosterior and deprecate utilities in AbstractInfer · Issue #1930 · pyro-ppl/pyro

1Financial forecasting with probabilistic programming and Pyro

1machine learning - Bias and variance in leave-one-out vs K-fold cross validation - Cross Validated

1predictive models - Differences between cross validation and bootstrapping to estimate the prediction error - Cross Validated

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1Early prediction and longitudinal modeling of preeclampsia from multiomics

1Predicting cell populations in single cell mass cytometry data

2BoostEMM-Transparent Boosting using Exceptional Model Mining.

1VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

1Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy

2Deep learning for inferring gene relationships from single-cell expression data

11MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images

1Differential dynamics of the maternal immune system in healthy pregnancy and preeclampsia

1ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI

2DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

5Understanding Where Your Classifier Does (Not) Work--The SCaPE Model Class for EMM

2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING

2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models

14Supervised random walks: predicting and recommending links in social networks

2b+ WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring—Preliminary results and analysis

1b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring

2Using markov chains for link prediction in adaptive web sites

1To explain or to predict?

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