Bayesian Inference Research Articles

Fast and Accurate Evacuation Planning Algorithm with Bayesian Optimization

In this work, we propose a method for generating an evacuation plan at a high speed to realize safe and swift evacuation in the event of a large-scale disaster such as an earthquake and its accompanying tsunami. Existing conventional methods have several problems. Simulation-based methods that use agents and methods that use existing time expansion networks have high computational costs, which makes it difficult for evacuation routes to be immediately changed according to the effects of disasters such as collapsed buildings and roads. Although heuristics with reduced calculation costs are also being researched, they may result in very long evacuation completion times and cannot generate optimal evacuation plans. We guarantee the optimal solution by reducing the number of maximum flow problem calculations, which constitute the bottleneck for methods using the existing time expansion network, through the use of the Bayesian optimization machine learning method. This reduces the calculation cost of the entire algorithm. The performance of our method is evaluated from the two viewpoints of the evacuation completion time, which indicates the quality of the evacuation plan, and the time required for the generation by the solution of the algorithm in computer experiments under multiple scenarios. In addition, the impact of the number of evacuees and the locations of the sinks are analyzed. We show that our method can quickly generate an optimal evacuation plan.

Spatio-temporal quasi-experimental methods for rare disease outcomes: the impact of reformulated gasoline on childhood haematologic cancer

Abstract Although some pollutants emitted in vehicle exhaust, such as benzene, are known to cause leukaemia in adults with high exposure levels, less is known about the relationship between traffic-related air pollution (TRAP) and childhood haematologic cancer. In the 1990s, the US EPA enacted the reformulated gasoline program in select areas of the U.S., which drastically reduced ambient TRAP in affected areas. This created an ideal quasi-experiment to study the effects of TRAP on childhood haematologic cancers. However, existing methods for quasi-experimental analyses can perform poorly when outcomes are rare and unstable, as with childhood cancer incidence. We develop Bayesian spatio-temporal matrix completion methods to conduct causal inference in quasi-experimental settings with rare outcomes. Selective information sharing across space and time enables stable estimation, and the Bayesian approach facilitates uncertainty quantification. We evaluate the methods through simulations and apply them to estimate the causal effects of TRAP on childhood leukaemia and lymphoma.

Exploring the influence of digital financial inclusion and technological progress on renewable energy consumption: a Bayesian quantile regression analysis

Exploring the influence of digital financial inclusion and technological progress on renewable energy consumption: a Bayesian quantile regression analysis

EXPRESS: From Social to Purchase: Customer Selection in Social Group Buying

Social scope group buying has emerged as an increasingly popular promotional strategy and has served as a new customer acquisition tool. In the service industry, companies use social group buying (SGB) to recruit new customers and promote full-price products. Through SGB activities, customers can trade their social capital to form buying groups, experience SGB-offered sample products, and further alleviate uncertainty regarding expensive full-price products before making a final purchase. We investigate this novel SGB phenomenon by examining customers’ decisions throughout the “experience-conversion” process. In collaboration with a leading online educational platform, we examine customers’ grouping behavior during SGB activities and analyze their subsequent purchases. Our analysis reveals an interesting pattern in which non-grouped customers have a higher proportion of full-price product purchases than grouped customers. We postulate that, in addition to observations from operational data, unobserved social information is important for gaining a deeper understanding of the customer behaviors underlying this pattern. Employing a Bayesian learning framework, we model customers’ three-stage discrete-choice decision-making processes and quantify two influential social information factors: social cost and social learning. By incorporating social information, our Bayesian learning model demonstrates improved effectiveness in assisting companies with accurately predicting final purchases in the conversion process. We provide actionable insights into how companies can employ our model to strategically tailor SGB designs by customer segments to increase overall purchase rates. Our study sheds light on the significance of social information in enhancing customer management and refining SGB design.

A Bayesian analysis integrating expert beliefs to better understand how new evidence ought to update what we believe: a use case of chiropractic care and acute lumbar disc herniation with early surgery.

A Bayesian approach may be useful in the study of possible treatment-related rare serious adverse events, particularly when there are strongly held opinions in the absence of good quality previous data. We demonstrate the application of a Bayesian analysis by integrating expert opinions with population-based epidemiologic data to investigate the association between chiropractic care and acute lumbar disc herniation (LDH) with early surgery. Experts' opinions were used to derive probability distributions of the incidence rate ratio (IRR) for acute LDH requiring early surgery associated with chiropractic care. A 'community of priors' (enthusiastic, neutral, and skeptical) was built by dividing the experts into three groups according to their perceived mean prior IRR. The likelihood was formed from the results of a population-based epidemiologic study comparing the relative incidence of acute LDH with early surgery after chiropractic care versus primary medical care, with sensitive and specific outcome case definitions and surgery occurring within 8- and 12-week time windows after acute LDH. The robustness of results to the community of priors and specific versus sensitive case definitions was assessed. The most enthusiastic 25% of experts had a prior IRR of 0.42 (95% credible interval [CrI], 0.03 to 1.27), while the most skeptical 25% of experts had a prior IRR of 1.66 (95% CrI, 0.55 to 4.25). The Bayesian posterior estimates across priors and outcome definitions ranged from an IRR of 0.39 (95% CrI, 0.21 to 0.68) to an IRR of 1.40 (95% CrI, 0.52 to 2.55). With a sensitive definition of the outcome, the analysis produced results that confirmed prior enthusiasts' beliefs and that were precise enough to shift prior beliefs of skeptics. With a specific definition of the outcome, the results were not strong enough to overcome prior skepticism. A Bayesian analysis integrating expert beliefs highlighted the value of eliciting informative priors to better understand how new evidence ought to update prior existing beliefs. Clinical epidemiologists are encouraged to integrate informative and expert opinions representing the end-user community of priors in Bayesian analyses, particularly when there are strongly held opinions in the absence of definitive scientific evidence.

Deep active learning for multi label text classification.

Given a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC. Training a high-quality deep MLTC model typically demands large-scale labeled data. And comparing with annotations for single-label data samples, annotations for multi-label samples are typically more time-consuming and expensive. Active learning can enable a classification model to achieve optimal prediction performance using fewer labeled samples. Although active learning has been considered for deep learning models, there are few studies on active learning for deep multi-label classification models. In this work, for the deep MLTC model, we propose a deep Active Learning method based on Bayesian deep learning and Expected confidence (BEAL). It adopts Bayesian deep learning to derive the deep model's posterior predictive distribution and defines a new expected confidence-based acquisition function to select uncertain samples for deep MLTC model training. Moreover, we perform experiments with a BERT-based MLTC model, where BERT can achieve satisfactory performance by fine-tuning in various classification tasks. The results on benchmark datasets demonstrate that BEAL enables more efficient model training, allowing the deep model to achieve training convergence with fewer labeled samples.

Bayesian Inference for Estimating Heat Sources Through Temperature Assimilation

Abstract This paper utilizes a Bayesian inference framework to address the two-dimensional (2D) steady-state heat conduction problem, focusing on the estimation of unknown distributed heat sources in a thermally conducting medium with uniform conductivity. The goal is to infer the locations, strength, and shape of heaters by assimilating temperature data in Euclidean space, employing a Fourier series to represent each heater's shape. The Markov Chain Monte Carlo (MCMC) method, incorporating the random-walk Metropolis–Hasting (MH) algorithm and parallel tempering, is utilized for posterior distribution exploration in both unbounded and wall-bounded domains. It is found that multiple solutions arise in cases where the number of temperature sensors is less than the number of unknown states. Moreover, smaller heaters introduce greater uncertainty in estimated strength. To address the challenge of estimating the heater's strength and shape simultaneously due to their strong correlation, our method incorporates sharp priors on one to ensure accurate and feasible solutions of the other. The diffusive nature of heat conduction smooths out any deformations in the temperature contours, especially in the presence of multiple heaters positioned near each other, impacting convergence. In wall-bounded domains with Neumann boundary conditions, the inference of heater parameters tends to be more accurate than in unbounded domains.

Phylogenetic relationships of Neogene hamsters (Mammalia, Rodentia, Cricetinae) revealed under Bayesian inference and maximum parsimony

There is an ongoing debate about the internal systematics of today’s group of hamsters (Cricetinae), following new insights that are gained based on molecular data. Regarding the closely related fossil cricetids, however, most studies deal with only a limited number of genera and statements about their possible relationships are rare. In this study, 41 fossil species from the Late Miocene to the Pliocene, belonging to seven extinct cricetine genera, Collimys, Rotundomys, Neocricetodon, Pseudocricetus, Cricetulodon, Apocricetus and Hattomys are analysed in a phylogenetic framework using traditional maximum parsimony and Bayesian inference approaches. Following thorough model testing, a relaxed-clock Bayesian inference analysis is performed under tip-dating to estimate divergence times simultaneously. Furthermore, so-called ‘rogue’ taxa are identified and excluded from the final trees to improve the informative value of the shown relationships. Based on these resulting trees, the fit of the topologies to the stratigraphy is assessed and the ancestral states of the characters are reconstructed under a parsimonious approach and stochastic character mapping. The overall topologies resulting from Bayesian and parsimonious approaches are largely congruent to each other and confirm the monophyly of most of the genera. Additionally, synapomorphies can be identified for each of these genera based on the ancestral state reconstructions. Only Cricetulodon turns out to be paraphyletic, while ‘Cricetulodon’ complicidens is a member of Neocricetodon. Lastly, this work makes a contribution to a debate that went on for decades, as the genus Kowalskia can be confirmed as junior synonym of Neocricetodon.

Optimising bearing degradation prediction: a bayesian vector autoregressive-assisted DeepAR model approach

Abstract Accurate prediction of the remaining useful life of bearings is crucial for the maintenance of rotating machinery. Current research often involves extracting multidimensional features from vibration signals to construct health degradation indicator, and health indicator is directly used for degradation prediction. However, constructed health indicator exhibit complex nonlinearity and fail to utilize the correlations with the original multidimensional features. To address this limitation, we propose an integrated prediction framework, known as the bayesian vector autoregression-assisted DeepAR method. The proposed two step prediction framework combines the advantages of covariance prediction and temporal correlation prediction, and realizes the full utilization of multidimensional features correlation information. Additionally, the proposed approach utilizes the intrinsic properties of statistical models to constrain improve the prediction results of deep learning methods, achieving nonlinear modeling of the degradation indicator while maintaining the stability of the prediction results. This process of prediction, re-estimation and adaptive adjustment achieves a balance in model performance. Finally, experimental studies using the PRONOSTIA and XJTU-SY datasets validate the effectiveness of the proposed method in improving the prediction accuracy of bearing health degradation indicators and enhancing the stability of multistep prediction in deep model.

Adaptive range gating based on variational Bayesian inference for space debris ranging with spaceborne single-photon LiDAR.

To enhance the accuracy of space debris localization, spaceborne single-photon LiDAR (SSPL) presents a promising technique for accurate target ranging. Extended Kalman filtering (EKF) plays a crucial role in range gating under high dynamic and nonlinear motion conditions of space debris, ensuring accurate state estimation and prior distance data. However, unknown and time-varying statistics of process and measurement noise significantly degrade state estimation accuracy, posing risks of filter divergence and reduced photon reception, ultimately rendering range gating ineffective. To address this challenge, we propose an adaptive range gating method based on variational Bayesian adaptive extended Kalman filtering (ARG-VBAEKF). This method leverages variational Bayesian (VB) posterior approximation to estimate the joint distribution of state and noise. Simulation results demonstrate that ARG-VBAEKF achieves accurate state and noise estimation, thereby effectively enhancing range gating performance in SSPL-based space debris ranging.

A defective cure rate quantile regression model for male breast cancer data

In this article, we particularly address the problem of assessing the impact of different prognostic factors, such as clinical stage and age, on the specific survival times of men with breast cancer when cure is a possibility. To this end, we developed a quantile regression model for survival data in the presence of long-term survivors based on the generalized Gompertz distribution in a defective version, which is conveniently reparametrized in terms of the q-th quantile and then linked to covariates via a logarithm link function. This proposal allows us to obtain how each variable affects the survival times in different quantiles. In addition, we are able to study the effects of covariates on the cure rate as well. We consider Markov Chain Monte Carlo methods to develop a Bayesian analysis in the proposed model and we evaluate its performance through Monte Carlo simulation studies. Finally, we illustrate the application of our model in a data set about male breast cancer from Brazil analyzed for the very first time.

Parameter estimation for allometric trophic network models: A variational Bayesian inverse problem approach

Abstract Differential equation models are powerful tools for predicting biological systems, capable of projecting far into the future and incorporating data recorded at arbitrary times. However, estimating these models' parameters from observations can be challenging because numerical methods are often required to approximate their solution. An example of such a model is the allometric trophic network model, for which studies considering its inverse problem are limited, particularly in the Bayesian framework. Here we develop a variational Bayesian method for parameter inference of the allometric trophic network model and explore how accurately we can recover its parameter values. We represent the model as a Bayesian neural network, which combines an artificial neural network with Bayesian inference, using a surrogate for the posterior distribution of model parameters, and train this model by evolutionary optimization to avoid potentially costly computation of the gradient with respect to the model parameters. Using synthetic data, we compare the accuracy of this variational inference to ordinary least squares estimation. To reduce the number of estimated parameters, we focus on the inference of functional response parameters. Our variational Bayesian method yields parameter estimates that are comparable to the ordinary least squares results in terms of accuracy. The method provides a promising approach for including uncertainty quantification in parameter estimation, which the simple ordinary least squares approach as it is does not address. Regardless of the method, potential multimodality of the inference problem is nonetheless important to keep in mind. The present study suggests a technique for parameter inference of ordinary differential equation models in the Bayesian context. We propose the method especially for validation of the allometric trophic network model against empirical data.

Biodiversity and Hemolytic Toxicity of the Genus Heterocapsa (Dinophyceae) in the Beibu Gulf, China

The dinoflagellate genus Heterocapsa includes several widely distributed and potentially toxic species associated with Harmful Algal Blooms (HABs), particularly affecting the Western Pacific Ocean. To reveal the biodiversity of Heterocapsa in Beibu Gulf, six strains were morphologically characterized using light and scanning electron microscopy, while large subunit rDNA (LSU rDNA) and internal transcribed spacer (ITS) were sequenced for phylogenetic analysis through maximum likelihood and Bayesian inferences. Two strains (BGERL169, BGERL170) were identified as Heterocapsa philippinensis ribotype I, three (BGERL171-BGERL173) as a new Heterocapsa philippinensis ribotype II, and one strain (BGERL174) as Heterocapsa pseudotriquetra. Cells of H. philippinensis were ovoid to spherical, yellowish-brown, with reticulate chloroplasts, and had a sausage-shaped nucleus positioned longitudinally along the dorsal side of the cell, and the theca was arranged in Po, cp, X, 5′, 3a, 7″, 6c, 5s, 5‴, 2⁗. Additionally, BGERL169 and BGERL171 showed no hemolytic toxicity in rabbit erythrocyte lysis assays. To the best of our knowledge, this research provides the first morphological and phylogenetic analysis of H. philippinensis, including the identification of a new ribotype, as well as the discovery of H. pseudotriquetra in Chinese waters. The findings contribute to the understanding of Heterocapsa species biogeography and toxicity in Chinese waters, offering valuable data for future HAB monitoring in Beibu Gulf.

AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.

Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology, particularly in early mammalian development, where tissue-level differentiation arises from seemingly cell-autonomous mechanisms. In this study, we present a multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) differentiation into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. Our framework models key regulatory and tissue-scale interactions in a biophysically realistic fashion, capturing the inherent stochasticity of intracellular gene expression and intercellular signaling, while efficiently simulating these processes by advancing event-driven simulation techniques. Leveraging the power of Simulation-Based Inference (SBI) through the AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, we conduct a large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features of ICM specification. Our results reveal mechanistic insights into how the combined action of autocrine and paracrine FGF4 signaling coordinates stochastic gene expression at the cellular scale to achieve robust and reproducible ICM patterning at the tissue scale. We further demonstrate that the ICM exhibits a specific time window of sensitivity to exogenous FGF4, enabling lineage proportions to be adjusted based on timing and dosage, thereby extending current experimental findings and providing quantitative predictions for both mutant and wild-type ICM systems. Notably, FGF4 signaling not only ensures correct EPI-PRE lineage proportions but also enhances ICM resilience to perturbations, reducing fate-proportioning errors by 10-20% compared to a purely cell-autonomous system. Additionally, we uncover a surprising role for variability in intracellular initial conditions, showing that high gene-expression heterogeneity can improve both the accuracy and precision of cell-fate proportioning, which remains robust when fewer than 25% of the ICM population experiences perturbed initial conditions. Our work offers a comprehensive, spatial-stochastic description of the biochemical processes driving ICM differentiation and identifies the necessary conditions for its robust unfolding. It also provides a framework for future exploration of similar spatial-stochastic systems in developmental biology.

Molecular Detection and Genotyping of Chlamydia psittaci in Birds in Buenos Aires City, Argentina

Chlamydia psittaci is a bacterium that infects several species of birds and mammals. It is the causal agent of avian chlamydiosis and psittacosis in humans and it is globally distributed. Chlamydia psittaci is one of the main zoonotic pathogens transmitted by birds. In Argentina, there has been limited research on the prevalence and genetic variability of C. psittaci. The aim of this study was to detect and genotype C. psittaci using molecular techniques in birds living in Buenos Aires City, Argentina, during the period 2012–2015. A descriptive, observational, retrospective and cross-sectional study was carried out. A total of 983 bird samples submitted for diagnosis of avian chlamydiosis were analyzed. The frequency of C. psittaci was 12.54% and 7.89% in Psittaciformes and Columbiformes, respectively. A 348 bp region of the ompA gene was sequenced in positive samples. Molecular genotyping was performed through a Bayesian phylogenetic analysis. Of the 983 bird samples, 83 were positive for C. psittaci and 44 could be sequenced. The genotypes found were A, B, and E. Despite the high levels of host specificity, we found six psittacids with genotype B and one pigeon with genotype A, reflecting the affiliative interaction between Psittaciformes and Columbiformes. This study represents the first survey reporting the presence of C. psittaci in birds within the largest and most populous city in Argentina.

Genomic characteristics of Salmonella enterica serovar Blockley.

Non-typhoidal Salmonella (NTS) is a significant cause of foodborne illness worldwide, with increasing antimicrobial resistance posing a public health concern. Salmonella enterica serovar Blockley (S. Blockley) is relatively uncommon, and its antimicrobial resistance profile and population structure have been understudied. This study presents a comprehensive genomic analysis of 264 S. Blockley isolates from diverse geographical regions to elucidate antimicrobial resistance patterns and population structure. Bayesian analysis classified these genomes into 10 distinct groups (BAPS A to BAPS J), further categorized into two lineages, R and S. Lineage R comprised six BAPS clusters (BAPSs A-F), predominantly found in Asia and Africa, all of which harbored the azithromycin resistance gene mph(A) and other resistance determinants. In contrast, lineage S, lacking mph(A), comprised the remaining four BAPS clusters, which were primarily found in Europe and the Americas. Several types of mutations in gyrA were found in lineage R, which were specific to BAPS clusters. These BAPS clusters exhibited distinct geographic distributions, with BAPS B, BAPS D, and BAPS E unique to China, Taiwan, and Japan, respectively, while BAPS H and BAPS I were predominantly found in the United States. Temporal phylogenetic analysis suggested that lineage R diverged in the 1980s, with notable microevolutionary changes. The presence of a genomic island with mph(A), aph(3')-Ia, aph(3")-Ib, aph(6)-Id, and tet(A) in lineage R underscores the public health threat, highlighting a need for continuous surveillance.IMPORTANCEAntimicrobial resistance in Salmonella is a global public health concern. In this study, we focused on serovar Blockley, and a whole-genome analysis revealed its global population structure. The results revealed the existence of azithromycin-resistant strains, which were characterized both phylogenetically and geographically. The resistance genes were transmitted via genomic islands, and their micro-scale evolution was also revealed. Our findings are the first to reveal the dissemination of antimicrobial resistance genes, including azithromycin, in serovar Blockley, and provide valuable insights into understanding the spread of antimicrobial resistance.

Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study.

Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values. A Bayesian bias analysis combines the prior distribution with the data's likelihood function whilst a Monte Carlo bias analysis samples the bias parameters directly from the prior distribution. No study has compared a Monte Carlo bias analysis to a Bayesian bias analysis in the context of MNAR missingness. We illustrate an accessible probabilistic bias analysis using the Monte Carlo bias analysis approach and a well-known imputation method. We designed a simulation study based on a motivating example from the UK Biobank study, where a large proportion of the outcome was missing and missingness was suspected to be MNAR. We compared the performance of our Monte Carlo bias analysis to a principled Bayesian bias analysis, complete case analysis (CCA) and multiple imputation (MI) assuming missing at random. As expected, given the simulation study design, CCA and MI estimates were substantially biased, with 95% confidence interval coverages of 7-48%. Including auxiliary variables (i.e., variables not included in the substantive analysis that are predictive of missingness and the missing data) in MI's imputation model amplified the bias due to assuming missing at random. With reasonably accurate and precise information about the bias parameter, the Monte Carlo bias analysis performed as well as the Bayesian bias analysis. However, when very limited information was provided about the bias parameter, only the Bayesian bias analysis was able to eliminate most of the bias due to MNAR whilst the Monte Carlo bias analysis performed no better than the CCA and MI. The Monte Carlo bias analysis we describe is easy to implement in standard software and, in the setting we explored, is a viable alternative to a Bayesian bias analysis. We caution careful consideration of choice of auxiliary variables when applying imputation where data may be MNAR.

On the posterior property of the Rician distribution

The Rician distribution, a well-known statistical distribution frequently encountered in fields like magnetic resonance imaging and wireless communications, is particularly useful for describing many real phenomena such as signal process data. In this paper, we introduce objective Bayesian inference for the Rician distribution parameters, specifically the Jeffreys rule and Jeffreys prior are derived. We proved that the obtained posterior for the first priors led to an improper posterior while the Jeffreys prior led to a proper distribution. To evaluate the effectiveness of our proposed Bayesian estimation method, we perform extensive numerical simulations and compare the results with those obtained from traditional moment-based and maximum likelihood estimators. Our simulations illustrate that the Bayesian estimators derived from the Jeffreys prior provide nearly unbiased estimates, showcasing the advantages of our approach over classical techniques. Additionally, our framework incorporates the S.A.F.E. principles – Sustainable, Accurate, Fair, and Explainable – ensuring robustness, fairness, and transparency in predictive modelling.

Simulation of laser plasma wakefield acceleration with external injection based on Bayesian optimization

Abstract In laser wakefield acceleration (LWFA), injecting an external electron beam at certain energy is a promising approach to achieving high-quality electron beam with low energy spread and low emittance. In this paper, the process of laser wakefield acceleration with an external injection at 10 pC has been studied in simulations. A Bayesian optimization method is used to optimize the key laser and plasma parameters, so that the electron beam is accelerated to the expected energy with a small emittance and energy spread growth. The effect of rising edge of the plasma on the transverse properties of the electron beam is simulated and optimized in order to ensure that the external electron beam is injected into the plasma without significant emittance growth. Finally, a high-quality electron beam with an energy of 1.5 GeV, the normalized transverse emittance of 0.5 mm·mrad and the relative energy spread of 0.5% at 10 pC is obtained.

Genetic Features of the Bream Abramis brama from the Lake System of the Sebezhsky National Park

For the first time, the analysis of the genetic variability of microsatellite DNA for five loci in the bream Abramis brama (L.) from lakes Sebezhskoye and Necheritsa of the Sebezhsky National Park, Sebezhsky district of the Pskov region, included in the lake system, was carried out. The results obtained demonstrate the uniformity of estimates of the genetic variability of bream in the lake system. Estimates of the allelic diversity of microsatellite loci and the expected heterozygosity of local bream groups did not differ significantly. The total genetic differentiation of bream was θ = 0.004. 95% CI (-0.01; 0.01) and was significantly insignificant. The population-genetic structure based on the studied multilocus genotypes has not been revealed by the Bayesian analysis method. The result indicate a high level of gene flow between local bream groupings and make it possible to assume the presence of a genetically unified panmixed population in the Sebezh lakes system.