Bayesian MCMC Research Articles

Origin and early diversification of a high Andean rove-beetle clade: Corisantis gen. nov., phylogeny, and historical biogeography

A new genus, Corisantisgen. nov., within the Andean clade (AC) of the rove-beetle subtribe Philonthina, is described and illustrated, including 14 species. This new genus comprises six valid species formerly assigned to Belonuchus Nordmann (B. candens (Erichson, 1840) and B. pulchripennisBernhauer, 1908) and Philonthus (Ph. auripennis Bernhauer, 1916, Ph. caliensis Bernhauer, 1916, Ph. excellens Bernhauer, 1916, and Ph. whymperiSharp, 1891), along with nine newly described species: C. ancashensissp. nov., C. bongarensissp. nov., C. cajamarcanussp. nov., C. columbiensissp. nov., C. ecuatoriensissp. nov., C. grandissp. nov., C. levissp. nov., C. magdalensissp. nov., and C. napoensissp. nov. The four species originally classified under Philonthus are reassigned to Corisantis with the following new combinations: C. auripennis (Bernhauer, 1916) comb. nov., C. caliensis (Bernhauer, 1916) comb. nov., C. excellens (Bernhauer, 1916) comb. nov., and C. whymperi (Sharp, 1891) comb. nov. Additionally, Belonuchus pulchripennis is synonymized under Belonuchus candens (= B. pulchripennissyn. nov.), which is also transferred to Corisantis as C. candens (Erichson, 1840) comb. nov. Lectotypes are designated for B. pulchripennisBernhauer (1908), Philonthus candensErichson (1840) and Philonthus whymperiSharp (1891). This study provides diagnoses, redescriptions or descriptions, illustrations, a distribution map, and an identification key for the species of Corisantis. Phylogenetic analyses, employing three methodologies—Maximum Parsimony, Maximum Likelihood, and Bayesian Inference—consistently support the monophyly of Corisantis and confirm its sister-group relationship with Leptopeltoides Chani-Posse and Asenjo, with both genera being sister to Leptopeltus Bernhauer. Additionally, biogeographical analyses using two methodologies—BBM (Bayesian Binary MCMC) and S-DIVA (Statistical Dispersal-Vicariance Analysis)—suggest that the South American Transition Zone was the center of origin and early diversification for these three genera.

Behavioural Change Piecewise Constant Spatial Epidemic Models

Human behaviour significantly affects the dynamics of infectious disease transmission as people adjust their behavior in response to outbreak intensity, thereby impacting disease spread and control efforts. In recent years, there have been efforts to incorporate behavioural change into spatio-temporal individual-level models within a Bayesian MCMC framework. In this past work, parametric spatial risk functions were employed, depending on strong underlying assumptions regarding disease transmission mechanisms within the population. However, selecting appropriate parametric functions can be challenging in real-world scenarios, and incorrect assumptions may lead to erroneous conclusions. As an alternative, non-parametric approaches offer greater flexibility. The goal of this study is to investigate the utilization of semi-parametric spatial models for infectious disease transmission, integrating an “alarm function” to account for behavioural change based on infection prevalence over time within a Bayesian MCMC framework. In this paper, we discuss findings from both simulated and real-life epidemics, focusing on constant piecewise distance functions with fixed change points. We also demonstrate the selection of the change points using the Deviance Information Criteria (DIC).

Cosmology in modified f (𝒢) gravity: a late-time cosmic phenomena

ABSTRACT In this work, we present a method for numerically solving the Friedmann equations of modified $f(\mathcal {G})$ gravity in the presence of pressureless matter. This method enables us to predict the redshift behaviour of the Hubble expansion rate. To evaluate the credibility of the model, we applied a Bayesian MCMC technique using late-time cosmic observations to impose limitations on the free parameters of the Gauss–Bonnet model. Our results suggest that the $f(\mathcal {G})$ model can reproduce the low-redshift behaviour of the standard Lambda cold dark matter ($\Lambda$CDM) model, but there are significant differences at high redshifts, leading to the absence of a standard matter-dominated epoch. We also examined the profiles of cosmographic parameters using the model parameter values from the standard range to verify the intermediate epochs. Our analysis shows that the highly promising $f(\mathcal {G})$ model is a feasible candidate for explaining the current epochs. We presented a dynamical system analysis framework to examine the stability of the model. Our study identified critical points depicting various phases of the Universe and explained the evolutionary epochs. We demonstrated that the model effectively captures the evolution of energy components over cosmic time, supporting its validity as an alternate explanation for the observed acceleration of the Universe.

AddiVortes: (Bayesian) Additive Voronoi Tessellations

The Additive Voronoi Tessellations (AddiVortes) model is a multivariate regression model that uses Voronoi tessellations to partition the covariate space in an additive ensemble model. Unlike other partition methods, such as decision trees, this has the benefit of allowing the boundaries of the partitions to be non-orthogonal and non-parallel to the covariate axes. The AddiVortes model uses a similar sum-of-tessellations approach and a Bayesian backfitting MCMC algorithm to the BART model. We utilize regularization priors to limit the strength of individual tessellations and accepts new models based on a likelihood. The performance of the AddiVortes model is illustrated through testing on several data sets and comparing the performance to other models along with a simulation study to verify some of the properties of the model. In many cases, the AddiVortes model outperforms random forests, BART and other leading black-box regression models when compared using a range of metrics. Supplementary materials for this article are available online.

TOI-3568 b: A super-Neptune in the sub-Jovian desert

The sub-Jovian desert is a region in the mass-period and radius-period parameter space that typically encompasses short-period ranges between super-Earths and hot Jupiters, and exhibits an intrinsic dearth of planets. This scarcity is likely shaped by photoevaporation caused by the stellar irradiation received by giant planets that have migrated inward. We report the detection and characterization of TOI-3568 b, a transiting super-Neptune with a mass of 26.4 ± 1.0 M⊕, a radius of 5.30 ± 0.27 R⊕, a bulk density of 0.98 ± 0.15 g cm−3, and an orbital period of 4.417965 (5) d situated in the vicinity of the sub-Jovian desert. This planet orbiting a K dwarf star with solar metallicity was identified photometrically by the Transiting Exoplanet Survey Satellite (TESS). It was characterized as a planet by our high-precision radial-velocity (RV) monitoring program using MAROON-X at Gemini North, supplemented with additional observations from the SPICE large program with SPIRou at CFHT. We performed a Bayesian MCMC joint analysis of the TESS and ground-based photometry, and MAROON-X and SPIRou RVs, to measure the orbit, radius, and mass of the planet, as well as a detailed analysis of the high-resolution flux and polarimetric spectra to determine the physical parameters and elemental abundances of the host star. Our results reveal TOI-3568 b to be a hot super-Neptune rich in hydrogen and helium, with a core of heavier elements of between 10 and 25 M⊕ in mass. We analyzed the photoevaporation status of TOI-3568 b and find that it experiences one of the highest extreme-ultraviolet (EUV) luminosities among planets with a mass of Mp < 2 MNep, yet it has an evaporation lifetime exceeding 5 Gyr. Positioned in the transition between two significant populations of exoplanets on the mass-period and energy diagrams, this planet presents an opportunity to test theories concerning the origin of the sub-Jovian desert.

1191P Efficient lung cancer stage prediction and outcome informatics with Bayesian deep learning and MCMC method

1191P Efficient lung cancer stage prediction and outcome informatics with Bayesian deep learning and MCMC method

PyIRoGlass: An open-source, Bayesian MCMC algorithm for fitting baselines to FTIR spectra of basaltic-andesitic glasses

PyIRoGlass: An open-source, Bayesian MCMC algorithm for fitting baselines to FTIR spectra of basaltic-andesitic glasses

Spatial Prediction of Diameter Distributions for the Alpine Protection Forests in Ebensee, Austria, Using ALS/PLS and Spatial Distributional Regression Models

A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data were used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. In the distributional regression approach, covariates can enter the model in a flexible form, such as via nonlinear smooth curves, cyclic smooths, or spatial effects. Supported by Bayesian diagnostics DIC and WAIC, nonlinear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. With BAMLSS, spatial interval predictions of the DBH distribution at any new geo-locations were enabled via straightforward access to the posterior predictive distributions of the model terms and by offering simple plug-in solutions for new covariate values. A cross-validation analysis validated the robustness of the proposed method’s parameter estimation and out-of-sample prediction. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape. Therefore, the intensity of final felling needs to be increased to reduce shading from the dense, overmature shelter trees and to promote sunlight for the young regeneration trees.

A GPR-based emulator for semi-numerical reionization code SCRIPT: parameter inference from 21 cm data

Semi-numerical models of reionization typically involve a large number of unknown parameters whose values are constrained by comparing with observations. Increasingly often, exploring this parameter space using semi-numerical simulations can become computationally intensive, thus necessitating the use of emulators. In this work, we present a likelihood emulator based on Gaussian Process Regression (GPR) for our semi-numerical reionization code, SCRIPT, and use it for parameter inference using mock 21 cm power spectrum data and Bayesian MCMC analysis. A unique aspect of our methodology is the utilization of coarse resolution simulations to identify high-probability regions within the parameter space, employing only a moderate amount of computational time. Samples drawn from these high-probability regions are used to construct the training set for the emulator. The subsequent MCMC using this GPR-trained emulator is found to provide parameter posteriors that agree reasonably well with those obtained using conventional MCMC. The computing time for the analysis, which includes both generation of training sets and training the emulator, is reduced by approximately an order of magnitude. This methodology is particularly advantageous in scenarios where one wants to use different parametrizations of reionization models and/or needs to start with broad prior distributions on the parameters, offering an efficient and effective means of parameter inference.

Utilizing Bayesian Modeling and MCMC for Accurate Characterization of Naturally Occurring Radionuclides Reference Background Levels in Mining Areas

Utilizing Bayesian Modeling and MCMC for Accurate Characterization of Naturally Occurring Radionuclides Reference Background Levels in Mining Areas

Inferring delays in partially observed gene regulation processes.

Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality. We develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components. Our code is implemented in R and is freely available with a simple example data at https://github.com/Mathbiomed/SimMCMC.

Phylogeography and demographic history of the cyprinid fish Barbodes semifasciolatus: implications for the history of landform changes in south mainland China, Hainan and Taiwan

Hainan Island and Taiwan Island are adjacent to the southern margin of mainland China and Vietnam. During glacial periods, global sea levels dropped, allowing that the land bridges connected the continental island and mainland, connecting rivers and providing dispersal opportunities that shaped the origin and diversification of freshwater fishes. Barbodes semifasciolatus is distributed in various water systems of Vietnam, Hainan, Taiwan, and southern mainland China and is restricted to the southern region of the Min River. Our study aimed to evaluate the genetic diversity and phylogeography of B. semifasciolatus using the mtDNA cyt b gene (1,141 bp). A total of 107 haplotypes were identified from 395 specimens in 23 populations, and high haplotype diversity (1.000) and low nucleotide diversity (0.0134) were detected. Mitochondrial phylogenetic analysis and haplotype network analyses revealed three major lineages according to geographical distribution. Lineage A was mainly distributed in Hainan Island, Vietnam and the southern region of the Pearl River in mainland China. Lineage B was distributed only in southeastern Hainan Island. Lineage C was distributed in the coastal rivers of mainland China and Taiwan. We suggest that the river in the Guangdong region is a colonization route in South Taiwan and that the populations distributed in the Pearl River region moved southward to Hainan Island and Vietnam based on the network and Bayesian binary MCMC (BBM) analysis. Our demographic history results indicated that the populations of B. semifasciolatus experienced a bottleneck event following a recent population expansion (DECINC model) supported by ABC analysis. We suggest that sea-level changes exerted pronounced effects on the demography of B. semifasciolatus on the continental island and in the mainland during the late Pleistocene glacial cycles.

Study on Bayesian Skew-Normal Linear Mixed Model and Its Application in Fire Insurance.

Fire insurance is a crucial component of property insurance, and its rating depends on the forecast of insurance loss claim data. Fire insurance loss claim data have complicated characteristics such as skewness and heavy tail. The traditional linear mixed model is commonly difficult to accurately describe the distribution of loss. Therefore, it is crucial to establish a scientific and reasonable distribution model of fire insurance loss claim data. In this study, the random effects and random errors in the linear mixed model are firstly assumed to obey the skew-normal distribution. Then, a skew-normal linear mixed model is established using the Bayesian MCMC method based on a set of U.S. property insurance loss claims data. Comparative analysis is conducted with the linear mixed model of logarithmic transformation. Afterward, a Bayesian skew-normal linear mixed model for Chinese fire insurance loss claims data is designed. The posterior distribution of claim data parameters and related parameter estimation are employed with the R language JAGS package to obtain the predicted and simulated loss claim values. Finally, the optimization model in this study is used to determine the insurance rate. The results demonstrate that the model established by the Bayesian MCMC method can overcome data skewness, and the fitting and correlation with the sample data are better than the log-normal linear mixed model. Hence, it can be concluded that the distribution model proposed in this paper is reasonable for describing insurance claims. This study innovates a new approach for calculating the insurance premium rate and expands the application of the Bayesian method in the fire insurance field.

Nexus between tourism and ecological footprint in RCEP: Fresh evidence from Bayesian MCMC random-effects sampling

Nexus between tourism and ecological footprint in RCEP: Fresh evidence from Bayesian MCMC random-effects sampling

Applying Monte Carlo Simulations to a Small Data Analysis of a Case of Economic Growth in COVID-19 Times.

Studies on the going-on COVID-19 pandemic face small sample issues. In this context, Bayesian estimation is considered a viable alternative to frequentist estimation. Demonstrating the Bayesian approach's advantage in dealing with this problem, our research conducted a case study concerning ASEAN economic growth during the COVID-19 pandemic. By using Monte Carlo standard errors and interval hypothesis testing to check parameter bias within a Bayesian MCMC simulation study, the author obtained significant conclusions as follows: first, in insufficient sample sizes, in contrast to frequentist estimation, the Bayesian framework can offer meaningful results, that is, expansionary monetary and contractionary fiscal policies are positively associated with economic growth; second, in the face of a small sample, by incorporating more information into prior distributions for the model parameters, Bayesian Monte Carlo simulations perform so far better than naïve Bayesian and frequentist estimation; third, in case of a correctly specified prior, the inferences are robust to different prior specifications. The author strongly recommends applying specific informative priors to Bayesian analyses, particularly in small sample investigations.

Theoretical challenges in single molecule.

Biological processes are directed by molecular actors conspiring, often in small numbers, to achieve tasks relevant to life. Yet these actors are not directly observable. Within the fluorescence paradigm, multiple layers of stochasticity lie between what we care about (molecular actor behavior) and what we observe (detector counts). These hierarchically organized layers include, but are not limited to, incomplete labeling, label photo-physics, optical aberrations, and detector noise. As a direct consequence, it is not possible to speak of discovering unique models of molecular behavior from data. Indeed many models are consistent with the data and our goal as theorists is to assign probabilities to candidate models based on observation. Here we give an overview of hierarchical Bayesian inference models and MCMC strategies for single molecule experiments with an emphasis on nonparametric Bayesian analysis of single photon smFRET highlighting a sequence of three papers to appear in Biophysical Reports.

Spillover shifts in the FX market: Implication for the behavior of a safe haven currency

This paper explores the behavior of safe haven currencies by analyzing shock transmission among major currencies. To capture state-dependent directional spillovers, we incorporate Markov regime-switching parameters into the spillover model and estimate them using a Bayesian MCMC algorithm. By considering weekly data from September 2000 to March 2020, we find that the Japanese yen and the Swiss franc, both of which yield relatively high excess returns in times of crisis, exhibit larger reductions of shock transmission and reception during periods of high-volatility than during periods of low-volatility. This implies that the safe haven currencies insulate themselves from shocks from other currencies by reducing interdependence across the FX market in crisis.

Reconstructing the Temporal Origin and the Transmission Dynamics of the HIV Subtype B Epidemic in St. Petersburg, Russia.

The HIV/AIDS epidemic in Russia is among the fastest growing in the world. HIV epidemic burden is non-uniform in different Russian regions and diverse key populations. An explosive epidemic has been documented among people who inject drugs (PWID) starting from the mid-1990s, whereas presently, the majority of new infections are linked to sexual transmission. Nationwide, HIV sub-subtype A6 (previously called AFSU) predominates, with the increasing presence of other subtypes, namely subtype B and CRF063_02A. This study explores HIV subtype B sequences from St. Petersburg, collected from 2006 to 2020, in order to phylogenetically investigate and characterize transmission clusters, focusing on their evolutionary dynamics and potential for further growth, along with a socio-demographic analysis of the available metadata. In total, 54% (107/198) of analyzed subtype B sequences were found grouped in 17 clusters, with four transmission clusters with the number of sequences above 10. Using Bayesian MCMC inference, tMRCA of HIV-1 subtype B was estimated to be around 1986 (95% HPD 1984-1991), whereas the estimated temporal origin for the four large clusters was found to be more recent, between 2001 and 2005. The results of our study imply a complex pattern of the epidemic spread of HIV subtype B in St. Petersburg, Russia, still in the exponential growth phase, and in connection to the men who have sex with men (MSM) transmission, providing a useful insight needed for the design of public health priorities and interventions.

Effects of various types of nanomaterials on PCM melting process in a thermal energy storage system for solar cooling application using CFD and MCMC methods

• A vertical shell and tube LHTES unit where D-mannitol was utilized as the PCM on the shell side was numerically analyzed. • Ag, Cu, Al 2 O 3 , CuO, TiO 2 , GNP, MWCNT, and SWCNT were added to PCM to enhance its thermophysical properties. • CFD and, a method based on Bayesian inference, was adopted by coding the Bayesian MCMC simulation for predicting the melting time. • Adding carbon-based nanomaterials to pure PCM reduced the melting time by about 50%, while metal nanoparticles impaired the melting performance. • Adding metal oxide nanoparticles did not add any essential advantage to the LHTES system. Latent heat thermal energy storage (LHTES) systems are attractive for bridging the energy supply and demand gap. In such systems, reducing storage time is critical, especially for solar applications. Accordingly, this study mainly aims to employ various nano-additives, including metal (Ag and Cu) and metal-oxide (Al 2 O 3 , CuO, and TiO 2 ) nanoparticles and carbon-based nanomaterials (GNP, MWCNT, SWCNT), to improve the thermophysical properties of pure phase change materials (PCM) to accelerate the melting process. For this purpose, the energy storage performance was numerically analyzed in a vertical shell and tube LHTES unit where D-mannitol was utilized as the PCM on the shell side. Dynalene-ht was employed as a heat transfer fluid (HTF) in the tube. Using computational fluid dynamics (CFD) modeling, transient variations in liquid fraction, PCM temperature, and total melting time were investigated under the impact of the following parameters: the thermophysical properties and volume fraction of nanomaterials, Re and the inlet temperature of HTF. In addition, a methodology based on Bayesian inference was adopted by coding the Bayesian MCMC simulation to create proper models for predicting the melting time. The numerical results showed that adding carbon-based nanomaterials to pure PCM reduced the melting time by about 50%, while metal nanoparticles impaired the melting performance. It was also observed that adding metal oxide nanoparticles did not add any essential advantage to the LHTES system. This research will help design TES applications in the operating temperature range of 160–200 °C, especially in solar cooling systems.

Phylogeographic analyses of an epiphytic foliose lichen show multiple dispersal events westward from the Hengduan Mountains of Yunnan into the Himalayas.

Lobaria pindarensis is an endemic species of the Himalayas and the Hengduan Mountains. Little information is available on the phylogeography genetics and colonization history of this species or how its distribution patterns changed in response to the orographic history of the Himalayas and Hengduan Mountains. Based on samples covering a major part of the species' distribution range, we used 443 newly generated sequences of nine loci for molecular coalescent analyses in order to reconstruct the evolutionary history of L. pindarensis, and to reconstruct the species' ancestral phylogeographic distributions using Bayesian binary MCMC analyses. The results suggest that current populations originated from the Yunnan region of the Hengduan Mountains in the middle Pliocene, and that the Himalayas of Bhutan were colonized by a lineage that diverged from Yunnan ca. 2.72 Ma. The analysis additionally indicates that the Nepal and Xizang areas of the Himalayas were colonized from Yunnan as well, and that there was later a second dispersal event from Yunnan to Bhutan. We conclude that the change in climate and habitat related to the continuous uplift of the Himalayas and the Hengduan Mountains in the late Pliocene and middle Pleistocene influenced the geographic distribution pattern of L. pindarensis.