Probabilistic programming in Python using PyMC3

Salvatier, John; Wiecki, Thomas V.; Fonnesbeck, Christopher

doi:10.7717/peerj-cs.55

Cited by 2,150 publications

(1,256 citation statements)

References 7 publications

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“…Cluster analysis of reanalysis data has been conducted using the community package. MCMC regression has been performed with the pyMC3 package (Salvatier et al, 2016). The corresponding references and data citations are provided in Table S2.…”

Section: Resultsmentioning

confidence: 99%

“…Since some of the considered clusters of paleoclimate archives do not cover the full Common Era, we can furthermore use the parameter distributions of the full set of clusters as priors to find the new distributions of the reduced set of CLD values, thus utilizing the knowledge of the full data for cases of lower data availability. For performing the MCMC regression, we use the pyMC3 package (Salvatier et al, 2016) with a NUTS (Hoffman and Gelman, 2014) with 10 4 samples with one-quarter of these as burn-in.…”

Section: Statistical Modelling By Regressionmentioning

confidence: 99%

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Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks

2017

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Abstract. Obtaining reliable reconstructions of long-term atmospheric circulation changes in the North Atlantic region presents a persistent challenge to contemporary paleoclimate research, which has been addressed by a multitude of recent studies. In order to contribute a novel methodological aspect to this active field, we apply here evolving functional network analysis, a recently developed tool for studying temporal changes of the spatial co-variability structure of the Earth's climate system, to a set of Late Holocene paleoclimate proxy records covering the last two millennia. The emerging patterns obtained by our analysis are related to long-term changes in the dominant mode of atmospheric circulation in the region, the North Atlantic Oscillation (NAO). By comparing the time-dependent inter-regional linkage structures of the obtained functional paleoclimate network representations to a recent multi-centennial NAO reconstruction, we identify co-variability between southern Greenland, Svalbard, and Fennoscandia as being indicative of a positive NAO phase, while connections from Greenland and Fennoscandia to central Europe are more pronounced during negative NAO phases. By drawing upon this correspondence, we use some key parameters of the evolving network structure to obtain a qualitative reconstruction of the NAO long-term variability over the entire Common Era (last 2000 years) using a linear regression model trained upon the existing shorter reconstruction.

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Section: Resultsmentioning

confidence: 99%

Section: Statistical Modelling By Regressionmentioning

confidence: 99%

Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks

2017

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“…For our implementation we used PyMC3 (Salvatier et al 2016) for the model specification and MCMC sampling. For data manipulation, linear algebra, and statistical and numerical computation we used NumPy (Walt et al 2011), SciPy (Walt et al 2011), Pandas (McKinney 2010 and Theano (Al-Rfou et al 2016).…”

Section: Methodsmentioning

confidence: 99%

Automatic Survey-invariant Classification of Variable Stars

Benavente

Protopapas

Pichara

2017

ApJ

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Machine learning techniques have been successfully used to classify variable stars on widely-studied astronomical surveys. These datasets have been available to astronomers long enough, thus allowing them to perform deep analysis over several variable sources and generating useful catalogs with identified variable stars. The products of these studies are labeled data that enable supervised learning models to be trained successfully. However, when these models are blindly applied to data from new sky surveys their performance drops significantly. Furthermore, unlabeled data becomes available at a much higher rate than its labeled counterpart, since labeling is a manual and time-consuming effort. Domain adaptation techniques aim to learn from a domain where labeled data is available, the source domain, and through some adaptation perform well on a different domain, the target domain. We propose a full probabilistic model that represents the joint distribution of features from two surveys as well as a probabilistic transformation of the features between one survey to the other. This allows us to transfer labeled data to a study where it is not available and to effectively run a variable star classification model in a new survey. Our model represents the features of each domain as a Gaussian mixture and models the transformation as a translation, rotation and scaling of each separate component. We perform tests using three different variability catalogs: EROS, MACHO, and HiTS, presenting differences among them, such as the amount of observations per star, cadence, observational time and optical bands observed, among others.

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“…The Bayesian inference takes the observed products and explanatory variables as input and outputs posterior probability dis- Gelman, 2014;Salvatier et al, 2016). We sample two independent chains with 2000 samples each, which standard quality controls (divergences, chain mixing) indicate is sufficient.…”

Section: Bayesian Inferencementioning

confidence: 99%

Estimating time-dependent vegetation biases in the SMAP soil moisture product

Zwieback¹,

Colliander²,

Cosh³

et al. 2018

Preprint

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Abstract. Remotely sensed soil moisture products are influenced by vegetation and how it is accounted for in the retrieval, which is a potential source of time-variable biases. To estimate such complex, time-variable error structures from noisy data, we introduce a Bayesian extension to triple collocation in which the systematic errors and noise terms are not constant but vary with explanatory variables. We apply the technique to the SMAP soil moisture product over croplands, hypothesizing that errors in the vegetation correction during the retrieval leave a characteristic fingerprint in the soil moisture time series. We 5 find that time-variable offsets and sensitivities are commonly associated with an imperfect vegetation correction. Especially the changes in sensitivity can be large, with seasonal variations of up to 40%. Variations of this size impede the seasonal comparison of soil moisture dynamics and the detection of extreme events. Also, estimates of vegetation-hydrology coupling can be distorted, as the SMAP soil moisture has larger R 2 values with a biomass proxy than the in-situ data, whereas noise alone would induce the opposite effect. We conclude that complex biases can be present in soil moisture products and that they 10 should be accounted for in observational and modelling studies.

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Probabilistic programming in Python using PyMC3

Cited by 2,150 publications

References 7 publications

Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks

Reconstructing Late Holocene North Atlantic atmospheric circulation changes using functional paleoclimate networks

Automatic Survey-invariant Classification of Variable Stars

Estimating time-dependent vegetation biases in the SMAP soil moisture product

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