Laplace's rule of succession in information geometry

Laplace's "add-one" rule of succession modifies the observed frequencies in a sequence of heads and tails by adding one to the observed counts. This improves prediction by avoiding zero probabilities and corresponds to a uniform Bayesian prior on the parameter. The canonical Jeffreys prior corresponds to the "add-one-half" rule. We prove that, for exponential families of distributions, such Bayesian predictors can be approximated by taking the average of the maximum likelihood predictor and the \emph{sequential normalized maximum likelihood} predictor from information theory. Thus in this case it is possible to approximate Bayesian predictors without the cost of integrating or sampling in parameter space

On Coupling FCA and MDL in Pattern Mining

International audiencePattern Mining is a well-studied field in Data Mining and Machine Learning. The modern methods are based on dynamically updating models, among which MDL-based ones ensure high-quality pattern sets. Formal concepts also characterize patterns in a condensed form. In this paper we study MDL-based algorithm called Krimp in FCA settings and propose a modified version that benefits from FCA and relies on probabilistic assumptions that underlie MDL. We provide an experimental proof that the proposed approach improves quality of pattern sets generated by Krimp

On Model Selection, Bayesian Networks, and the Fisher Information Integral

We study BIC-like model selection criteria and in particular, their refinements that include a constant term involving the Fisher information matrix. We perform numerical simulations that enable increasingly accurate approximation of this constant in the case of Bayesian networks. We observe that for complex Bayesian network models, the constant term is a negative number with a very large absolute value that dominates the other terms for small and moderate sample sizes. For networks with a fixed number of parameters, d, the leading term in the complexity penalty, which is proportional to d, is the same. However, as we show, the constant term can vary significantly depending on the network structure even if the number of parameters is fixed. Based on our experiments, we conjecture that the distribution of the nodes’ outdegree is a key factor. Furthermore, we demonstrate that the constant term can have a dramatic effect on model selection performance for small sample sizes.Peer reviewe

Cadophora margaritata sp. nov. and other fungi associated with the longhorn beetles Anoplophora glabripennis and Saperda carcharias in Finland

Symbiosis with microbes is crucial for survival and development of wood-inhabiting longhorn beetles (Coleoptera: Cerambycidae). Thus, knowledge of the endemic fungal associates of insects would facilitate risk assessment in cases where a new invasive pest occupies the same ecological niche. However, the diversity of fungi associated with insects remains poorly understood. The aim of this study was to investigate fungi associated with the native large poplar longhorn beetle (Saperda carcharias) and the recently introduced Asian longhorn beetle (Anoplophora glabripennis) infesting hardwood trees in Finland. We studied the cultivable fungal associates obtained from Populus tremula colonised by S. carcharias, and Betula pendula and Salix caprea infested by A. glabripennis, and compared these to the samples collected from intact wood material. This study detected a number of plant pathogenic and saprotrophic fungi, and species with known potential for enzymatic degradation of wood components. Phylogenetic analyses of the most commonly encountered fungi isolated from the longhorn beetles revealed an association with fungi residing in the Cadophora-Mollisia species complex. A commonly encountered fungus was Cadophora spadicis, a recently described fungus associated with wood-decay. In addition, a novel species of Cadophora, for which the name Cadophora margaritata sp. nov. is provided, was isolated from the colonised wood.Peer reviewe

Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible