Bayesian MCMC Research Articles

Resilience Dynamic Assessment Based on Precursor Events: Application to Ship LNG Bunkering Operations

The focus of the present paper is the development of a resilience framework suitable to be applied in assessing the safety of ship LNG (Liquefied Natural Gas) bunkering process. Ship propulsion considering LNG as a possible fuel (with dual fuel marine engines installed on board) has favored important discussions about the LNG supply chain and delivery on board to the ship power plant. Within this context, a resilience methodological approach is outlined, including a case study application, to demonstrate its actual effectiveness. With specific reference to the operative steps for LNG bunkering operations in the maritime field, a dynamic model based on Bayesian inference and MCMC simulations can be built, involving the probability of operational perturbations, together with their updates based on the hard (failures) and soft (process variables deviations) evidence emerging during LNG bunkering operations. The approach developed in this work, based on advanced Markov Models and variational fitting algorithms, has proven to be a useful and flexible tool to study, analyze and verify how much the perturbations of systems and subsystems can be absorbed without leading to failure.

Spatial Origin and Diversification of the Lycocerus fainanus Species Group (Coleoptera, Cantharidae), with Descriptions of Four New Species from China and Vietnam.

Simple SummaryLycocerus Gorham, 1889 has about 320 species worldwide at present, of which a small part have been attributed into 13 species groups, but the rest remain uncertain in their status. In the present study, five previously known species and another four new species from China and Vietnam were classified to the Lycocerus fainanus species group, based on their morphological characters. Then the distribution pattern of this group was analyzed with ArcGIS techniques. It showed that all species were constricted to the subtropical area of the Oriental region and distributed in both Taiwan and the continent. Further, the ancestral distribution range was reconstructed with the aid of the Reconstruct Ancestral State in Phylogenies (RASP) software, based on a phylogeny of morphological data. The results showed that the spatial origin was located in northern Vietnam and southwest China. Subsequently, two dispersal routes occurred: the first originated from the continent and then to Taiwan, the second from Taiwan to the continent, and the divergence between the species was caused by dispersal and vicariance. Unfortunately, it was difficult to estimate the timing of origin because of the lack of direct fossil evidence and molecular data.Five previously known species were attributed to the Lycocerus fainanus species group, including L. inopaciceps (Pic 1926), L. oberthueri (Gorham 1889), L. oudai (Švihla 2004), L. metallipennis (Fairmaire 1887), and L. nigripes (Wittmer, 1995). Four new species of this group were discovered from China and Vietnam, L. binotatus sp. nov., L. testacicollis sp. nov., L. daliensis sp. nov., and L. vietnamensis sp. nov. An updated key to all species was provided. A geographical distribution map is presented, which shows that all the members were located between 18.69041–33.93441° N, and between 98.61413–121.77102° E. The ancestral geographical range was reconstructed based on a phylogeny of morphological data by the Bayesian Binary MCMC method. The result showed that the spatial origin of L. fainanus species group was probably located in northern Vietnam and southwest China. The divergence of the species in southwest China and Taiwan was caused by vicariance about 24 Ma ago, when the latter was separated in the Qinghai-Tibet Plateau, and the remaining species of mainland China all originated from Taiwan after traveling around Southeast Asia and back to China. Nevertheless, this conclusion should be verified when fossil evidence and molecular data are available.

A kinematic analysis of ionized extraplanar gas in the spiral galaxies NGC 3982 and NGC 4152

ABSTRACT We present a kinematic study of ionized extraplanar gas in two low-inclination late-type galaxies (NGC 3982 and NGC 4152) using integral field spectroscopy data from the DiskMass H α sample. We first isolate the extraplanar gas emission by masking the H α flux from the regularly rotating disc. The extraplanar gas emission is then modelled in the 3D position–velocity domain using a parametric model described by three structural and four kinematic parameters. Best-fitting values for the model are determined via a Bayesian MCMC approach. The reliability and accuracy of our modelling method are carefully determined via tests using mock data. We detect ionized extraplanar gas in both galaxies, with scale heights $0.83^{+0.27}_{-0.40}\, \mathrm{kpc}$ (NGC 3982) and $1.87^{+0.43}_{-0.56}\, \mathrm{kpc}$ (NGC 4152) and flux fraction between the extraplanar gas and the regularly rotating gas within the disc of 27 and 15 per cent, respectively, consistent with previous determinations in other systems. We find lagging rotation of the ionized extraplanar gas in both galaxies, with vertical rotational gradients $-22.24^{+6.60}_{-13.13} \, \mathrm{km\, s^{-1}\, kpc^{-1}}$ and $-11.18^{+3.49}_{-4.06}\, \mathrm{km\, s^{-1}\, kpc^{-1}}$, respectively, and weak evidence for vertical and radial inflow in both galaxies. The above results are similar to the kinematics of the neutral extraplanar gas found in several galaxies, though this is the first time that 3D kinematic modelling of ionized extraplanar gas has been carried out. Our results are broadly consistent with a galactic fountain origin combined with gas accretion. However, a dynamical model is required to better understand the formation of ionized extraplanar gas.

The Unique Amphipoda and Tanaidacea (Crustacea: Peracarida) Associated With the Brown Algae Dictyota sp. From the Oceanic Trindade Island, Southwestern Atlantic, With Biogeographic and Phylogenetic Insights

Oceanic islands are known by their unique evolutionary histories and high endemicity caused by isolation. This is the first survey on the biodiversity of Peracarida from Trindade Island, a volcanic island located about 1,160 km off the Brazilian coast, with the first reports of Tanaidacea from the island and the description ofSynapseudes isissp. nov. and three new species of Amphipoda (Ampithoe thaixsp. nov.,Elasmopus gabrielisp. nov., andEusiroides lucaisp. nov.). The results of the phylogenetic analysis ofSynapseudesbased on morphological characters and its biogeography through the Bayesian Binary MCMC analysis (BBM) suggested an Indo-Pacific origin for the genus. Finally, the biodiversity of Trindade Island is compared to that of the Abrolhos Archipelago, the closest islands from the coast on the continental shelf, suggesting a high endemicity of Peracarida, corresponding to 44% of Amphipoda and 50% of Tanaidacea species in the island of Trindade.

Western Tethys origin, tropical Asia and tropical America disjunction in Berchemia and reinstatement of Phyllogeiton (Rhamneae, Rhamnaceae)

AbstractDespite the well‐known biogeographic pattern in the Northern Hemisphere of disjunct distributions in plants between eastern Asia and North America, many details regarding the evolution of the disjunction between lineages of tropical Asia and tropical America remain poorly understood. Berchemia (Rhamnaceae), comprising ca. 32 species, is distributed predominantly in tropical regions of east to southeast Asia and North America. However, the phylogeny and biogeography of this genus are still not clear. Furthermore, fossils of the genus have been found in Europe, making it an ideal group to understand the biogeographical story of tropical disjunction in the Northern Hemisphere. With two nuclear (ETS, ITS) and three plastid (petA‐psbJ, psbA‐trnH, trnL‐F) DNA regions, we first reconstructed the phylogeny of Berchemia with Bayesian inference and maximum likelihood approaches. Our results indicated that Berchemia, excluding the African species B. discolor and B. zeyheri, which were placed into the reinstated Phyllogeiton, was well‐supported as monophyletic. Divergence times and ancestral areas were inferred using a Bayesian uncorrelated lognormal relaxed molecular clock model and Bayesian binary MCMC method, respectively. The core Berchemia likely originated in the Western Tethys during the middle Eocene, then formed the tropical Asia and tropical America disjunction during the Oligocene and further diversified in Asia from the early Miocene on. The formation and diversification of the core Berchemia likely resulted from a mixture of geologic events, such as the close of the Tethys Ocean, disappearance of the North Atlantic Land Bridges, uplift of the Qinghai‐Tibetan Plateau, and climatic events, such as the establishment and enhancement of Asian monsoons.

Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.

Molecular epidemiological characteristics of echovirus 6 in mainland China: extensive circulation of genotype F from 2007 to 2018

Echovirus 6 (E6) is associated with various clinical diseases and is frequently detected in environmental sewage. Despite its high prevalence in humans and the environment, little is known about its molecular phylogeography in mainland China. In this study, 114 of 21,539 (0.53%) clinical specimens from hand, foot, and mouth disease (HFMD) cases collected between 2007 and 2018 were positive for E6. The complete VP1 sequences of 87 representative E6 strains, including 24 strains from this study, were used to investigate the evolutionary genetic characteristics and geographical spread of E6 strains. Phylogenetic analysis based on VP1 nucleotide sequence divergence showed that, globally, E6 strains can be grouped into six genotypes, designated A to F. Chinese E6 strains collected between 1988 and 2018 were found to belong to genotypes C, E, and F, with genotype F being predominant from 2007 to 2018. There was no significant difference in the geographical distribution of each genotype. The evolutionary rate of E6 was estimated to be 3.631 × 10-3 substitutions site-1 year-1 (95% highest posterior density [HPD]: 3.2406 × 10-3-4.031 × 10-3 substitutions site-1 year-1) by Bayesian MCMC analysis. The most recent common ancestor of the E6 genotypes was traced back to 1863, whereas their common ancestor in China was traced back to around 1962. A small genetic shift was detected in the Chinese E6 population size in 2009 according to Bayesian skyline analysis, which indicated that there might have been an epidemic around that year.

Unsupervised Learning for MRI Brain Tumor Segmentation with Spatially Variant Finite Mixture Model in Reversible Jump MCMC Algorithm

MRI brain tumor segmentation is an important topic in medical image processing. Manual segmentation is risky and time-consuming when the MRI is in low quality. The automatic segmentation can be a solution to manage this problem. This paper proposed an improved modeling approach for unsupervised learning trough Spatially Variant Finite Mixture Model (SVFMM). The main contribution is the automation of the SVFMM algorithm to find the optimum number of clusters. This is achieved by employing the birth-death random process in Bayesian Reversible Jump MCMC. Validation of the proposed model is done by calculating the Correct Classification Ration (CCR) in comparison to the original SVFMM and Gaussian Mixture Model (GMM). The proposed model provides similar performance in image segmentation compared to the original SVFMM but is better than GMM. However, SVFMM-RJMCMC is faster and more efficient in finding the optimum number of clusters.

The evolutionary history and phylogeographic pattern of Hippuris vulgaris: hybridization and long-distance dispersal from China

Hippuris vulgaris is a widespread aquatic plant in the Northern Hemisphere. Phylogenetic analysis on a low-copy nuclear gene (PHYB) indicated that this species originated from a hybridization between two unknown ancestral parents. Two distinct lineages (I and II) were detected using chloroplast intergenic regions, and their divergence time was 1.51 Ma. Lineage I was composed of the populations from North China, Qinghai-Tibetan Plateau (QTP), and Europe while lineage II included the populations from North China, Europe and Japan. Based on the results of the Bayesian binary MCMC (BBM) analysis, QTP was likely the original center of these two lineages. The possible dispersal route for lineage I was inferred to be directly from QTP to North China and Europe, respectively, while lineage II likely dispersed first to Japan and from there moved separately to North China and Europe. In this study, we illustrate two important evolutionary events of H. vulgaris: hybridization and long-distance dispersals.

Mechanical Ventilation Control Based on Estimated occupancy using a Carbon Dioxide Sensor

Ventilation is an important aspect to maintain good indoor air quality in a building. However, excessive ventilation causing high energy consumption of the HVAC system. The ASHRAE Standard provides a guideline to set the ventilation rate that depends on the occupants' number and space. Thus, quantification of the number of occupants is required to regulate the ventilation rate. In this study, the estimated number of occupants was estimated using a Bayesian MCMC method based on CO2 levels. The mass balance equation of the CO2 is used as a model for the calculation of Bayesian MCMC. The Bayesian method for estimating the occupants' number is tested in a 96,7 m3 office room equipped with a ventilation system. Thus the occupancy estimation and control of ventilation can be done in real-time. The test also includes conventional ventilation control based on CO2 levels directly without converting to the occupants' number. The ventilation rate based on the number of occupants at the present test chamber refers to ASHRAE 62.1. The test results show that ventilation controlled by the estimated number of occupants using the Bayesian method successfully conducted with ventilation rate per occupant closer to the ASHRAE 62.1 standard over conventional ventilation method

Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution

In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives that stem from interaction benefits of certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interactions are important factors for friendship formation or not. Second, in addition to homophily effects in terms of unobserved characteristics, inclusion of incentive effects in the network formulation also corrects possible friendship selection bias on activity outcomes under network interactions. A theoretical foundation of this unified model is based on a sub-game perfect equilibrium of a two-stage game. A tractable Bayesian MCMC approach is proposed for the estimation of the model, and we demonstrate its finite sample performance in a simulation study. We apply the model to study empirically American high school students' friendship networks from the Add Health dataset. We consider two activity variables, GPA and smoking frequency, and find a significant incentive effect from GPA, but not from smoking, on friendship formation. These results suggest that the benefit of interactions in academic learning is an important factor for friendship formation, whereas the interaction benefit of smoking is not. On the other hand, from the perspective of network interactions, both GPA and smoking frequency are subject to significant positive interaction (peer) effects.

A Bayesian Internal Model for Reserve Risk: An Extension of the Correlated Chain Ladder

The goal of this paper was to exploit the Bayesian approach and MCMC procedures to structure an internal model to quantify the reserve risk of a non-life insurer under Solvency II regulation. To this aim, we provide an extension of the Correlated Chain Ladder (CCL) model to the one-year time horizon. In this way, we obtain the predictive distribution of the next year obligations and we are able to assess a capital requirement compliant with Solvency II framework. Numerical results compare the one-year CCL with other traditional approaches, such as Re-Reserving and the Merz and Wüthrich formula. One-year CCL proves to be a legitimate alternative, providing values comparable with the more traditional approaches and more robust and accurate risk estimations, that embed external knowledge not present in the data and allow for a more precise and tailored representation of the risk profile of the insurer.

Heterogeneous Variable Selection in Nonlinear Panel Data Models: A Semi-Parametric Bayesian Approach

In this paper, we develop a general method for heterogeneous variable selection in Bayesian nonlinear panel data models. Heterogeneous variable selection refers to the possibility that subsets of units are unaffected by certain variables. It may be present in applications as diverse as health treatments, consumer choice-making, macroeconomics, and operations research. Our method additionally allows for other forms of cross-sectional heterogeneity. We consider a two-group approach for the model's unit-specific parameters: each unit-specific parameter is either equal to zero (heterogeneous variable selection) or comes from a Dirichlet process (DP) mixture of multivariate normals (other cross-sectional heterogeneity). We develop our approach for general nonlinear panel data models, encompassing multinomial logit and probit models, poisson and negative binomial count models, exponential models, among many others. For inference, we develop an efficient Bayesian MCMC sampler. In a Monte Carlo study, we find that our approach is able to capture heterogeneous variable selection whereas a standard'' DP mixture is not. In an empirical application, we find that accounting for heterogeneous variable selection and non-normality of the continuous heterogeneity leads to an improved in-sample and out-of-sample performance and interesting insights. These findings illustrate the usefulness of our approach.

On a Bivariate Hysteretic AR-GARCH Model with Conditional Asymmetry in Correlations

This paper investigates a conditionally dynamic asymmetric structure in correlations when multivariate time series follow a hysteretic autoregressive GARCH process that exhibits nonlinear switching in mean, volatility, and correlation. The hysteresis variable in the proposed model controls regime switching and time-varying delay. This new model allows for distinct responses to negative return shocks, as it can flexibly explore the possibility of asymmetry in the conditional correlation of two target variables. We employ an adaptive Bayesian MCMC method for the parameter estimation and quantile forecasting, which includes value-at-risk and volatility. We conduct backtesting to measure the effectiveness of value-at-risk forecasting, illustrate the proposed methods by using both simulated and real examples, and jointly measure for industry downside tail risk. Lastly, we evaluate the accuracy of the volatility forecast and determine whether there is persistence of conditional asymmetry in conditional correlation in the target time series.

Modeling the Control of TGF-β/Smad Nuclear Accumulation by the Hippo Pathway Effectors, Taz/Yap.

SummaryIntegration of transforming growth factor β (TGF-β) signals with those of other pathways allows for precise temporal and spatial control of gene expression patterns that drive development and homeostasis. The Hippo pathway nuclear effectors, Taz/Yap, interact with the TGF-β transcriptional mediators, Smads, to control Smad activity. Key to TGF-β signaling is the nuclear localization of Smads. Thus, to investigate the role of Taz/Yap in Smad nuclear accumulation, we developed mathematical models of Hippo and TGF-β cross talk. The models were based on experimental measurements of TGF-β-induced changes in Taz/Yap and Smad subcellular localization obtained using high-throughput immunofluorescence (IF) imaging in the mouse mammary epithelial cell line, EpH4. Bayesian MCMC DREAM parameter estimation was used to quantify the uncertainty in estimates of the kinetic parameters. Variation of the model parameters and statistical analysis show that our modeling predicts that Taz/Yap can alter TGF-β receptor activity and directly or indirectly act as nuclear retention factors.

Inference for cluster point processes with over- or under-dispersed cluster sizes

Cluster point processes comprise a class of models that have been used for a wide range of applications. While several models have been studied for the probability density function of the offspring displacements and the parent point process, there are few examples of non-Poisson distributed cluster sizes. In this paper, we introduce a generalization of the Thomas process, which allows for the cluster sizes to have a variance that is greater or less than the expected value. We refer to this as the cluster sizes being over- and under-dispersed, respectively. To fit the model, we introduce minimum contrast methods and a Bayesian MCMC algorithm. These are evaluated in a simulation study. It is found that using the Bayesian MCMC method, we are in most cases able to detect over- and under-dispersion in the cluster sizes. We use the MCMC method to fit the model to nerve fiber data, and contrast the results to those of a fitted Thomas process.

Measuring China's monetary policy uncertainty and its impact on the real economy

This paper proposes a measure of China's monetary policy uncertainty (MPU) based on a Bayesian MCMC estimation procedure, investigates the economic impact of MPU, and outlines the impact channels. Empirical findings suggest that MPU depresses China's economy via higher credit risk and lower economic growth. Moreover, the decrease in output is amplified and made more persistent when the economy is simultaneously exposed to higher credit risk. Because China has experienced massive credit expansion over the past decades, the economy is currently more vulnerable to the increase in MPU than it ever was.

Bayesian Estimation of the Functional Spatial Lag Model

Abstract The spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

A Bayesian sensitivity analysis of the effect of different random effects distributions on growth curve models

Growth curve data consist of repeated measurements of a continuous growth process of human, animal, plant, microbial or bacterial genetic data over time in a population of individuals. A classical approach for analysing such data is the use of non-linear mixed effects models under normality assumption for the responses. But, sometimes the underlying population that the sample is extracted from is an abnormal population or includes some homogeneous sub-samples. So, detection of original properties of the population is an important scientific question of interest. In this paper, a sensitivity analysis of using different parametric and non-parametric distributions for the random effects on the results of applying non-linear mixed models is proposed for emphasizing the possible heterogeneity in the population. A Bayesian MCMC procedure is developed for parameter estimation and inference is performed via a hierarchical Bayesian framework. The methodology is illustrated using a real data set on study of influence of menarche on changes in body fat accretion.

Investigation of Freight Agents’ Interaction Considering Partner Selection and Joint Decision Making

Freight transportation plays an increasingly important role in sustainable development. However, freight travel demand has not been understood comprehensively, due to its unique features: freight activities are the result of collaboration among freight agents. It distinguishes freight transportation from passenger transportation, in which travel decisions are made mostly by individuals. Specifically, two processes in the collaboration can be observed: partner selection and joint decision making. Using the supplier-customer collaboration as an example, partner selection is a process for suppliers and customers to evaluate their potential partners and select the best one. Joint decision making allows suppliers and customers to seek common interests and make compromises. As a traditional travel demand model cannot model the two processes effectively, this research develops an innovative econometric model, spatial matching model, to bridge the gap. The proposed model is specified based on freight agents’ behavioral, estimated by Bayesian MCMC methods, and demonstrated by numerical examples. The proposed model and estimation methods can recover the coefficient values in the econometric models, and establish the relationship between the influential factors and the observed matching behavior. The analysis improves the understanding of freight travel demand in a behavioral-consistent manner and enriches the body of freight demand modeling literature.