Direct Bayesian model reduction of smaller scale convective activity conditioned on large scale dynamics

We pursue a simplified stochastic representation of smaller scale convective activity conditioned on large scale dynamics in the atmosphere. For identifying a Bayesian model describing the relation of different scales we use a probabilistic approach (Gerber and Horenko, 2017) called Direct Bayesian Model Reduction (DBMR). The convective available potential energy (CAPE) is applied as large scale flow variable combined with a subgrid smaller scale time series for the vertical velocity. We found a probabilistic relation of CAPE and vertical up- and downdraft for day and night. The categorization is based on the conservation of total probability. This strategy is part of a development process for parametrizations in models of atmospheric dynamics representing the effective influence of unresolved vertical motion on the large scale flows. The direct probabilistic approach provides a basis for further research of smaller scale convective activity conditioned on other possible large scale drivers.

Probabilistic relation between co-speech gestures, pitch accents and information status

This study investigates the occurrence of co-speech gestures as a function of prosodic prominence (pitch accents) and discourse meaning (information status) in a clear and engaging speech style. Among several types of co-speech gestures, we examine non-referential gestures, which are claimed to be prosodic in nature (Shattuck-Hufnagel & Ren 2018). In particular, we want to find out to what extent these gestures co-occur with specific accent types and whether they are used to encode referential, lexical, or contrastive information. Our results show that the occurrence of gestures was highest for L+H*, followed by H*, !H*, and unaccented words. Gestures were accompanied by L* only in continuations. Also, co-speech gestures were more likely to occur with new or accessible, and especially contrastive, information than with given information. The patterns differed between the referential and lexical level of information status, though. In general, this study suggests that co-speech gestures contribute to the probabilistic encoding of a word’s information status in conjunction with pitch accents.

Investigating hot-Jupiter inflated radii with hierarchical Bayesian modelling

Context. As of today, hundreds of hot Jupiters have been found, yet the inflated radii of a large fraction of them remain unexplained. A number of mechanisms have been proposed to explain these anomalous radii, however most of these can only work under certain conditions and may not be sufficient to explain the most extreme cases. It is still unclear whether a single mechanism can sufficiently explain the entire distribution of radii, or whether a combination of these mechanisms is needed. Aims. We seek to understand the relationship of radius with stellar irradiation and mass and to find the range of masses over which hot Jupiters are inflated. We also aim to find the intrinsic physical scatter in their radii, caused by unobservable parameters, and to constrain the fraction of hot Jupiters that exhibit inflation. Methods. By constructing a hierarchical Bayesian model, we inferred the probabilistic relation between planet radius, mass, and incident flux for a sample of 286 gas giants. We separately incorporated the observational uncertainties of the data and the intrinsic physical scatter in the population. This allowed us to treat the intrinsic physical scatter in radii, due to latent parameters such as the heavy element fraction, as a parameter to be inferred. Results. We find that the planetary mass plays a key role in the inflation extent and that planets in the range ~0.37−0.98 MJ show the most inflated radii. At higher masses, the radius response to incident flux begins to decrease. Below a threshold of 0.37 ± 0.03 MJ we find that giant exoplanets as a population are unable to maintain inflated radii ≿1.4 RJ but instead exhibit smaller sizes as the incident flux is increased beyond 106 W m−2. We also find that below 1 MJ, there is a cut-off point at high incident flux beyond which we find no more inflated planets, and that this cut-off point decreases as the mass decreases. At incident fluxes higher than ~1.6 × 106 W m−2 and in a mass range 0.37−0.98 MJ, we find no evidence for a population of non-inflated hot Jupiters. Our study sheds a fresh light on one of the key questions in the field and demonstrates the importance of population-level analysis to grasp the underlying properties of exoplanets.

Probabilistic Relation between Triadic Closure and the Balance of Social Networks in Presence of Influence

Data analysis in social networking is a major research concern in todays' environment in the field of interpretation models of data for any network. Social networking includes only two types of relationships: firstly, a friendly relation with whom one is having a link that can be considered as a positive relation and secondly, a relationship with which one is not connected or so called one's enemies labelled as negative relationships. Balanced theorem of social networking claims that all the nodes in the social network can be divided into two sets: a friendship set and an enemy set and provides the global view of relationships. In this paper, the authors have shown a probabilistic model to show that the global view of social links does not only depend on negative and positive relations to be distinguished, but it also depends on influences parameters.

Shallow landslide's stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications

Due to their relatively unpredictable characteristics, shallow landslides represent a risk for human infrastructures. Multiple shallow landslides can be triggered by widespread intense precipitation events. The event of August 2005 in Switzerland is used in order to propose a risk model to predict the expected number of landslides based on the precipitation amounts and lithological units. The spatial distribution of rainfall is characterized by merging data coming from operational weather radars and a dense network of rain gauges with an artificial neural network. Lithologies are grouped into four main units, with similar characteristics. Then, from a landslide inventory containing more than 5000 landslides, a probabilistic relation linking the precipitation amount and the lithology to the number of landslides in a 1 km2 cell, is derived. In a next step, this relation is used to randomly redistribute the landslides using Monte Carlo simulations. The probability for a landslide to reach a building is assessed using stochastic geometry and the damage cost is assessed from the estimated mean damage cost using an exponential distribution to account for the variability. Although the model reproduces well the number of landslides, the number of affected buildings is underestimated. This seems to result from the human influence on landslide occurrence. Such a model might be useful to characterize the risk resulting from shallow landslides and its variability.

Shallow landslides stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications

Due to their relatively unpredictable characteristics, shallow-landslides represent a risk for human infrastructures. Multiple shallow-landslides can be triggered by large spread precipitation events. The event of August 2005 in Switzerland is used in order to propose a risk model to predict the expected number of landslides based on the precipitation amounts and lithological units. The spatial distribution of rainfall is characterized by blending data coming from operational weather radars and a dense network of rain gauges with an artificial neural network. Lithologies are grouped into four main units, with similar characteristics. Then, from a landslide inventory containing more than 5000 landslides, a probabilistic relation linking the precipitation amount and the lithology to the number of landslides in a 1 km2 cell, is obtained. In a next step, this relation is used to randomly redistribute the landslides using Monte-Carlo simulations. The probability for a landslide to reach a building is assessed using stochastic geometry and the damage cost is assessed from the estimated mean damage cost using an exponential distribution to account for the variability. Although the outputs reproduce well the number of landslides, the number of affected buildings is not reproduced by the model. This seems to results from the human influence on landslide occurrence. Such a model might be useful to characterize the risk resulting from shallow-landslides and its variability.