A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. We study the implementation of traffic assignment engineering in conjunction with the network stochastic model: first, we study the Bayesian algorithm theoretical model of control layer stripping in the network based on the discrete dynamic Bayesian algorithm theory and analyze the resource-sharing mechanism in different queuing rules; second, we study the extraction and evaluation theory of traffic assignment for the global view obtained by the control layer of the network and establish the Bayesian algorithm analysis model based on the traffic assignment; subsequently, the routing of bandwidth guarantee and delay guarantee in the network is studied based on Bayesian algorithm model and Bayesian algorithm network random traffic allocation theory. In this paper, a Bayesian algorithm estimation model based on Bayesian algorithm theory is constructed based on network random observed traffic assignment as input data. The model assumes that the roadway traffic distribution follows the network random principle, and based on this assumption, the likelihood function of the roadway online traffic under the network random condition is derived; the prior distribution of the roadway traffic is derived based on the maximum entropy principle; the posterior distribution of the roadway traffic is solved by combining the likelihood function and the prior distribution. The corresponding algorithm is designed for the model with roadway traffic as input, and the reliability of the algorithm is verified in the arithmetic example.

The 2019 Methane Budget And Uncertainties At 1 Degree Resolution And Each Country Through Bayesian Integration Of GOSAT Total Column Methane Data And A Priori Inventory Estimates

We present 2019 global methane (CH4) emissions and uncertainties, by sector, at 1-degree and country-scale resolution based on a Bayesian integration of satellite data and inventories. Globally, we find that agricultural and fire emissions are 227 +/− 19 Tg CH4/yr, waste is 50 +/− 7 Tg CH4/yr , anthropogenic fossil emissions are 82 +/− 12 Tg CH4/yr, and natural wetland/aquatic emissions are 180 +/− 10 Tg CH4/yr. These estimates are intended as a pilot dataset for the Global Stock Take in support of the Paris Agreement. However, differences between the emissions reported here and widely-used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite based emissions estimates. Calculation of emissions and uncertainties: We first apply a standard optimal estimation (OE) approach to quantify CH4 fluxes using Greenhouse Gases Observing Satellite (GOSAT) total column CH4 concentrations and the GEOS-Chem global chemistry transport model. Second, we use a new Bayesian algorithm that projects these posterior fluxes to emissions by sector to 1 degree and country-scale resolution. This algorithm can also quantify uncertainties from measurement as well as smoothing error, which is due to the spatial resolution of the top-down estimate combined with the assumed structure in the prior emission uncertainties. Detailed Results: We find that total emissions for approximately 58 countries can be resolved with this observing system based on the degrees-of-freedom for signal (DOFS) metric that can be calculated with our Bayesian flux estimation approach. We find the top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions. However, posterior emissions for these countries are mostly from agriculture, waste and fires (~129 Tg CH4/yr) with ~45 Tg CH4/yr from fossil emissions, as compared to prior inventory estimates of ~88 and 60 Tg CH4/yr respectively, primarily because the satellite observed concentrations are larger than expected in regions with substantive livestock activity. Differences are outside of 1-sigma uncertainties between prior and posterior for Brazil, India, and Russia but are consistent for China and the USA. The new Bayesian algorithm to quantify emissions from fluxes also allows us to “swap priors” if better informed or alternative priors and/or their covariances are available for testing. For example, recent bottom-up literature supposes greatly increased values for wetland/aquatic as well as fossil emissions. Swapping in priors that reflect these increased emissions results in posterior wetland emissions or fossil emissions that are inconsistent (differences greater than calculated uncertainties) with these increased bottom-up estimates, primarily because constraints related to the methane sink only allow total emissions across all sectors of ~560 Tg CH4/yr and because the satellite based estimate well constrains the spatially distinct fossil and wetland emissions. Given that this observing system consisting of GOSAT data and the GEOS-Chem model can resolve much of the different sectoral and country-wide emissions, with ~402 DOFS for the whole globe, our results indicate additional research is needed to identify the causes of discrepancies between these top-down and bottom-up results for many of the emission sectors reported here. In particular, the impact of systematic errors in the methane retrievals and transport model employed should be assessed where differences exist. However, our results also suggest that significant attention must be provided to the location and magnitude of emissions used for priors in top-down inversions; for example, poorly characterized prior emissions in one region and/or sector can affect top-down estimates in another because of the limited spatial resolution of these top-down estimates. Satellites such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO2M, Methane-Sat, or Carbon Mapper offer the promise of much higher resolution fluxes relative to GOSAT assuming they can provide data with comparable or better accuracy, thus potentially reducing this uncertainty from poorly characterized emissions. These higher resolution estimates can therefore greatly improve the accuracy of emissions by reducing smoothing error. Fluxes calculated from other sources can also in principal be incorporated in the Bayesian estimation framework demonstrated here for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.

Research on the Methods and Key Techniques of Web Archive Oriented Social Media Information Collection

Social media information collection and preservation is a hot issue in the field of Web Archive. This paper makes a comparative analysis of the different social media information collection methods, deeply analyzes the key techniques of the three important parts-collection, evaluation and preservation in the information collection process, and provides the solutions for the problems in the key techniques. Through analysis, the collection method suitable for the social media information is found. In terms of the problem that social websites impose restrictions on the call frequency of API, the paper provides solutions, for example, use the multiplexing mechanism, use the naive Bayesian algorithm to solve the spam filtering problem, and use MongoDB Dbased distributed storage to store collected massive data.

Mapping the gravitational-wave sky with LISA: A bayesian spherical harmonic approach

The millihertz gravitational-wave frequency band is expected to contain a rich symphony of signals with sources ranging from galactic white dwarf binaries to extreme mass ratio inspirals. Many of these gravitational-wave signals will not be individually resolvable. Instead, they will incoherently add to produce stochastic gravitational-wave confusion noise whose frequency content will be governed by the dynamics of the sources. The angular structure of the power of the confusion noise will be modulated by the distribution of the sources across the sky. Measurement of this structure can yield important information about the distribution of sources on galactic and extra-galactic scales, their astrophysics and their evolution over cosmic timescales. Moreover, since the confusion noise is part of the noise budget of LISA, mapping it will also be essential for studying resolvable signals. In this paper, we present a Bayesian algorithm to probe the angular distribution of the stochastic gravitational-wave confusion noise with LISA using a spherical harmonic basis. We develop a technique based on Clebsch-Gordan coefficients to mathematically constrain the spherical harmonics to yield a non-negative distribution, making them optimal for expanding the gravitational-wave power and amenable to Bayesian inference. We demonstrate these techniques using a series of simulations and analyses, including recovery of simulated distributed and localized sources of gravitational-wave power. We also apply this method to map the gravitational-wave foreground from galactic white-dwarfs using a simplified model of the galactic white dwarf distribution.

Comparative Analysis and Forecasting on the Death Rate of COVID-19 Patients in Nigeria Using Random Forest and Multinomial Bayesian Epidemiological Models

The novel COVID-19 (SARS-COV-2) is a disease currently ravaging the world, bringing unprecedented health and economic challenges to several nations. There are presently close to 175,000 reported cases in Nigeria with fatalities numbering over 2,163 persons. The main objective of this paper is to compare the analysis and predictive accuracy between the Random Forest and the Multinomial Bayesian Epidemiological model for a cumulative number of deaths for COVID-19 cases in Nigeria by identifying the underlying factors which may propagate future occurrences. It is worthy to note that the Random Forest algorithm is an ensemble learning approach for classification, regression, and other tasks that works by training a large number of decision trees G(t) while the Multinomial Bayesian algorithm provides an excellent theoretical framework for analyzing experimental data and the highlight of its success relies on its ability to integrate prior knowledge about the parameters of interest as a distribution function p(Ck|d).

STRONG: metagenomics strain resolution on assembly graphs

We introduce STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, from multiple metagenome samples. STRONG performs coassembly, and binning into metagenome assembled genomes (MAGs), and stores the coassembly graph prior to variant simplification. This enables the subgraphs and their unitig per-sample coverages, for individual single-copy core genes (SCGs) in each MAG, to be extracted. A Bayesian algorithm, BayesPaths, determines the number of strains present, their haplotypes or sequences on the SCGs, and abundances. STRONG is validated using synthetic communities and for a real anaerobic digestor time series generates haplotypes that match those observed from long Nanopore reads.