scholarly journals BAli-Phy version 3: Model-based co-estimation of Alignment and Phylogeny

2020 ◽  
Author(s):  
Benjamin D. Redelings
Keyword(s):  
Monte Carlo ◽  
Sequence Data ◽  
Source Code ◽  
Model Parameters ◽  
Ancestral State ◽  
State Reconstruction ◽  
Large Trees ◽  
New Models ◽  
Prior Selection ◽  
Selection For

AbstractSummaryWe describe improvements to BAli-Phy, a Markov chain Monte Carlo (MCMC) program that jointly estimates phylogeny, alignment, and other parameters from unaligned sequence data. Version 3 is substantially faster for large trees, and implements covarion models, RNA stem models, and other new models. It implements ancestral state reconstruction, allows prior selection for all model parameters, and can also analyze multiple genes simultaneously.AvailabilitySoftware is available for download at http://www.bali-phy.org. C++ source code is freely available on Github under the GPL2 [email protected]

scholarly journals The McBurr XII and Log-McBurr XII Models with Applications to Lifetime Data

2015 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Gauss M. Cordeiro ◽  
Edwin M. M. Ortega ◽  
G. G. Hamedani ◽  
Diogo A. Garcia
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Models ◽  
Real Data ◽  
Model Parameters ◽  
Lifetime Data ◽  
Fatigue Lifetime ◽  
Burr Xii Distribution ◽  
New Models ◽  
Lifetime Model

A new six-parameter extended fatigue lifetime model named the McDonald-Burr XII distribution is introduced, which generalizes the Burr XII, beta Burr XII (Parana\'iba {\it et al.}, 2011) and Kumaraswamy-Burr XII (Parana\'iba {\it et al.}, 2012) distributions. The proposed distribution is characterized in terms of truncated moments. We obtain the ordinary and incomplete moments and quantile and ge\-ne\-ra\-ting functions. We estimate the model parameters by maximum likelihood. A Monte Carlo simulation is performed to study the asymptotic normality of the estimates. We also propose an extended regression model based on the logarithm of the McDonald-Burr XII distribution that can be more realistic in the analysis of real data than other special regression models. Two applications to real data validate the importance of the new models.

scholarly journals A new lineage of mazaediate fungi in the Eurotiomycetes: Cryptocaliciomycetidae subclass. nov., based on the new species Cryptocalicium blascoi and the revision of the ascoma evolution

2021 ◽  
Vol 20 (7) ◽  
pp. 889-904
Author(s):  
M. Prieto ◽  
Javier Etayo ◽  
I. Olariaga
Keyword(s):  
New Species ◽  
Common Ancestor ◽  
External Surface ◽  
Evolutionary Relationships ◽  
New Order ◽  
Ancestral State ◽  
Multigene Phylogeny ◽  
State Reconstruction ◽  
Distinct Structure ◽  
Dark Violet

AbstractThe class Eurotiomycetes (Ascomycota, Pezizomycotina) comprises important fungi used for medical, agricultural, industrial and scientific purposes. Eurotiomycetes is a morphologically and ecologically diverse monophyletic group. Within the Eurotiomycetes, different ascoma morphologies are found including cleistothecia and perithecia but also apothecia or stromatic forms. Mazaediate representatives (with a distinct structure in which loose masses of ascospores accumulate to be passively disseminated) have evolved independently several times. Here we describe a new mazaediate species belonging to the Eurotiomycetes. The multigene phylogeny produced (7 gene regions: nuLSU, nuSSU, 5.8S nuITS, mtSSU, RPB1, RPB2 and MCM7) placed the new species in a lineage sister to Eurotiomycetidae. Based on the evolutionary relationships and morphology, a new subclass, a new order, family and genus are described to place the new species: Cryptocalicium blascoi. This calicioid species occurs on the inner side of loose bark strips of Cupressaceae (Cupressus, Juniperus). Morphologically, C. blascoi is characterized by having minute apothecioid stalked ascomata producing mazaedia, clavate bitunicate asci with hemiamyloid reaction, presence of hamathecium and an apothecial external surface with dark violet granules that becomes turquoise green in KOH. The ancestral state reconstruction analyses support a common ancestor with open ascomata for all deep nodes in Eurotiomycetes and the evolution of closed ascomata (cleistothecioid in Eurotiomycetidae and perithecioid in Chaetothyriomycetidae) from apothecioid ancestors. The appropriateness of the description of a new subclass for this fungus is also discussed.

scholarly journals Control variate selection for Monte Carlo integration

2021 ◽  
Vol 31 (4) ◽  
Author(s):  
Rémi Leluc ◽  
François Portier ◽  
Johan Segers
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Integration ◽  
Control Variate ◽  
Selection For

scholarly journals Statistical Analysis of Tritium Breeding Ratio Deviations in the DEMO Due to Nuclear Data Uncertainties

2021 ◽  
Vol 11 (11) ◽  
pp. 5234
Author(s):  
Jin Hun Park ◽  
Pavel Pereslavtsev ◽  
Alexandre Konobeev ◽  
Christian Wegmann
Keyword(s):  
Monte Carlo ◽  
Fusion Reactor ◽  
Nuclear Data ◽  
Data File ◽  
Nuclear Model ◽  
Model Parameters ◽  
Tritium Breeding ◽  
Tritium Breeding Ratio ◽  
Nuclear Data Library ◽  
Breeding Ratio

For the stable and self-sufficient functioning of the DEMO fusion reactor, one of the most important parameters that must be demonstrated is the Tritium Breeding Ratio (TBR). The reliable assessment of the TBR with safety margins is a matter of fusion reactor viability. The uncertainty of the TBR in the neutronic simulations includes many different aspects such as the uncertainty due to the simplification of the geometry models used, the uncertainty of the reactor layout and the uncertainty introduced due to neutronic calculations. The last one can be reduced by applying high fidelity Monte Carlo simulations for TBR estimations. Nevertheless, these calculations have inherent statistical errors controlled by the number of neutron histories, straightforward for a quantity such as that of TBR underlying errors due to nuclear data uncertainties. In fact, every evaluated nuclear data file involved in the MCNP calculations can be replaced with the set of the random data files representing the particular deviation of the nuclear model parameters, each of them being correct and valid for applications. To account for the uncertainty of the nuclear model parameters introduced in the evaluated data file, a total Monte Carlo (TMC) method can be used to analyze the uncertainty of TBR owing to the nuclear data used for calculations. To this end, two 3D fully heterogeneous geometry models of the helium cooled pebble bed (HCPB) and water cooled lithium lead (WCLL) European DEMOs were utilized for the calculations of the TBR. The TMC calculations were performed, making use of the TENDL-2017 nuclear data library random files with high enough statistics providing a well-resolved Gaussian distribution of the TBR value. The assessment was done for the estimation of the TBR uncertainty due to the nuclear data for entire material compositions and for separate materials: structural, breeder and neutron multipliers. The overall TBR uncertainty for the nuclear data was estimated to be 3~4% for the HCPB and WCLL DEMOs, respectively.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Automatic Prior Selection for Image Deconvolution: Statistical Modeling on Natural Images

2021 ◽  
pp. 108307
Author(s):  
Haegeun Lee ◽  
Jaeduk Han ◽  
Soonyoung Hong ◽  
Moon Gi Kang
Keyword(s):  
Statistical Modeling ◽  
Natural Images ◽  
Image Deconvolution ◽  
Prior Selection ◽  
Selection For

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

scholarly journals Modelling the spread of American foulbrood in honeybees

2013 ◽  
Vol 10 (88) ◽  
pp. 20130650 ◽  
Author(s):  
Samik Datta ◽  
James C. Bull ◽  
Giles E. Budge ◽  
Matt J. Keeling
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Control Strategies ◽  
Sir Model ◽  
Model Parameters ◽  
American Foulbrood ◽  
Inspection Period ◽  
Stochastic Sir ◽  
Stochastic Sir Model ◽  
The Impact

We investigate the spread of American foulbrood (AFB), a disease caused by the bacterium Paenibacillus larvae , that affects bees and can be extremely damaging to beehives. Our dataset comes from an inspection period carried out during an AFB epidemic of honeybee colonies on the island of Jersey during the summer of 2010. The data include the number of hives of honeybees, location and owner of honeybee apiaries across the island. We use a spatial SIR model with an underlying owner network to simulate the epidemic and characterize the epidemic using a Markov chain Monte Carlo (MCMC) scheme to determine model parameters and infection times (including undetected ‘occult’ infections). Likely methods of infection spread can be inferred from the analysis, with both distance- and owner-based transmissions being found to contribute to the spread of AFB. The results of the MCMC are corroborated by simulating the epidemic using a stochastic SIR model, resulting in aggregate levels of infection that are comparable to the data. We use this stochastic SIR model to simulate the impact of different control strategies on controlling the epidemic. It is found that earlier inspections result in smaller epidemics and a higher likelihood of AFB extinction.

scholarly journals Prior Selection for Vector Autoregressions

2015 ◽  
Vol 97 (2) ◽  
pp. 436-451 ◽  
Author(s):  
Domenico Giannone ◽  
Michele Lenza ◽  
Giorgio E. Primiceri
Keyword(s):  
Vector Autoregressions ◽  
Prior Selection ◽  
Selection For
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