Bayesian Markov Chain Monte Carlo Research Articles

The fitness landscape of the codon space across environments.

Fitness landscapes map the relationship between genotypes and fitness. However, most fitness landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the fitness effects of synonymous mutations and their potential impact on adaptation on a fitness landscape, we use a new software based on Bayesian Monte Carlo Markov Chain methods and re-estimate selection coefficients of all possible codon mutations across 9 amino acid positions in Saccharomyces cerevisiae Hsp90 across 6 environments. We quantify the distribution of fitness effects of synonymous mutations and show that it is dominated by many mutations of small or no effect and few mutations of larger effect. We then compare the shape of the codon fitness landscape across amino acid positions and environments, and quantify how the consideration of synonymous fitness effects changes the evolutionary dynamics on these fitness landscapes. Together these results highlight a possible role of synonymous mutations in adaptation and indicate the potential mis-inference when they are neglected in fitness landscape studies.

Parameter calibration in global soil carbon models using surrogate-based optimization

Abstract. Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12 % for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.

Frequency Analysis and Uncertainty Assessment of Annual Maximum Flood Series Using Bayesian MCMC Method

The calculation of flood quantiles and its uncertainty estimation are important subjects of hydraulic engineering planning and water resources management. In this study, the Bayesian theory is used to implement frequency analysis and uncertainty assessment of annual maximum flood series, the Generalized Extreme Value (GEV) distribution is considered as the flood frequency distribution line type, and the Markov chain Monte Carlo (MCMC) method based on Metropolis-Hastings algorithm is used to evaluate the GEV distribution parameters, then the posterior distributions of flood flow quantiles are used to calculate the point estimations and interval estimations of flood design values under different return periods. The results show that the fitting effect of the Bayesian MCMC method is the same as the maximum likelihood estimation (MLE), but the Bayesian MCMC more superior when the uncertainties were considered. Compared with the traditional methods of flood frequency analysis, the proposed Bayesian MCMC method provides not only the design flood estimated values, but also the confidence intervals of the estimated values. In addition, the lengths between upper confidence limits and estimated values are greater than the lower confidence limits and estimated values, this asymmetry is more realistic than the traditional methods such as the delta method, thus improve the reliability of flood frequency analysis.

Agrizero: an O2O adversarial framework and triangulation for agricultural forecasting

This study proposes an online to offline (O2O) learning framework, ArgiZero, based on three components: a generative adversarial network, an online auction market, and offline simulated agents (digital twins: buyers, farmers, natures, and markets). The generative time series and digital twins are massively generated in a manner of Monte Carlo but with extremely efficient algorithms. The goal of the generator is to produce time series that are statistically indistinguishable with the records from auction market. The goal of the discriminator is to develop a triangulation method based on semi-modeless assimilation to separate generated from actual time series. Most farmers believe agriculture is impossible to be planned because uncertainty, crowding, crisis, and risk cannot be foreseen and accommodated. This AgriZero framework alleviates the challenges through the techniques of Bayesian deep learning and data assimilation as well as the mega power of GPU computation. Thanks to Bayesian hierarchical estimation, which is akin to deep learning but more sophisticate and longer history. We are able to estimate human behaviour in the agents of buyers and farmers, natural disaster in the agents of natures, and price fluctuations in the agents of markets. The framework has been validated by a large amount of records in vegetable auctions of Taiwan and USA. The hierarchical Bayesian estimation and Monte Carlo Markov Chain particle filters used in hidden Markov model are appreciated during the massive construction of the most probable digital twins. The feature space mapping in wavelet time series, Bayesian deep learning in recurrent neural network, kernel induced Hamiltonian dynamics ABC, and hybrid SDE-kernel based forecasting for time series analysis embodies the particle generator in the GAN structure. We also apply time series clustering, RNN, bagging and boosting, and semi-modeless assimilation to assist the performance of the triangulated discriminator.

Molecular evolution and phylodynamics of hepatitis B virus infection circulating in Iran.

Previous local and national Iranian publications indicate that all Iranian hepatitis B virus (HBV) strains belong to HBV genotype D. The aim of this study was to analyze the evolutionary history of HBV infection in Iran for the first time, based on an intensive phylodynamic study. The evolutionary parameters, time to most recent common ancestor (tMRCA), and the population dynamics of infections were investigated using the Bayesian Monte Carlo Markov chain (BMCMC). The effective sample size (ESS) and sampling convergence were then monitored. After sampling from the posterior distribution of the nucleotide substitution rate and other evolutionary parameters, the point estimations (median) of these parameters were obtained. All Iranian HBV isolates were of genotype D, sub-type ayw2. The origin of HBV is regarded as having evolved first on the eastern border, before moving westward, where Isfahan province then hosted the virus. Afterwards, the virus moved to the south and west of the country. The tMRCA of HBV in Iran was estimated to be around 1894, with a 95% credible interval between the years 1701 and 1957. The effective number of infections increased exponentially from around 1925 to 1960. Conversely, from around 1992 onwards, the effective number of HBV infections has decreased at a very high rate. Phylodynamic inference clearly demonstrates a unique homogenous pattern of HBV genotype D compatible with a steady configuration of the decreased effective number of infections in the population in recent years, possibly due to the implementation of blood donation screening and vaccination programs. Adequate molecular epidemiology databases for HBV are crucial for infection prevention and treatment programs.

Tracing the stellar component of low surface brightness Milky Way dwarf galaxies to their outskirts

Aims. We present results from deep and very spatially extended CTIO/DECam g and r photometry (reaching out to ~2 mag below the oldest main-sequence turn-off and covering ~20 deg2) around the Sextans dwarf spheroidal galaxy. We aim to use this dataset to study the structural properties of Sextans overall stellar population and its member stars in different evolutionary phases, as well as to search for possible signs of tidal disturbance from the Milky Way, which would indicate departure from dynamical equilibrium. Methods. We performed the most accurate and quantitative structural analysis to-date of Sextans’ stellar components by applying Bayesian Monte Carlo Markov chain methods to the individual stars’ positions. Surface density maps are built by statistically decontaminating the sample through a matched filter analysis of the colour-magnitude diagram, and then analysed for departures from axisymmetry. Results. Sextans is found to be significantly less spatially extended and more centrally concentrated than early studies suggested. No statistically significant distortions or signs of tidal disturbances were found down to a surface brightness limit of ~31.8 mag/arcsec2 in V-band. We identify an overdensity in the central regions that may correspond to previously reported kinematic substructure(s). In agreement with previous findings, old and metal-poor stars such as Blue Horizontal Branch stars cover a much larger area than stars in other evolutionary phases, and bright Blue Stragglers (BSs) are less spatially extended than faint ones. However, the different spatial distribution of bright and faint BSs appears consistent with the general age and metallicity gradients found in Sextans’ stellar component. This is compatible with Sextans BSs having formed by evolution of binaries and not necessarily due to the presence of a central disrupted globular cluster, as suggested in the literature. We provide structural parameters for the various populations analysed and make publicly available the photometric catalogue of point-sources as well as a catalogue of literature spectroscopic measurements with updated membership probabilities.

Genetic structure of Apis mellifera carnica in Slovakia based on microsatellite DNA polymorphism

AbstractCarniolan honeybee (

Occupancy Estimation Based on Indoor CO2 Concentration: Comparison of Neural Network and Bayesian Methods

The number of occupants in a space can significantly affect ventilation control. Using neural network and Bayesian Markov chain Monte Carlo (MCMC) methods, this study estimates the number of occupants based on CO2 concentration in a room. The abilities of both methods to recognize the input-parameter characteristics are compared under certain circumstances, and the parameters are optimized to improve the estimation accuracy. The neural network trains an input dataset of CO2 concentrations, ventilation rates, and occupancy patterns with tapped delay lines. Meanwhile, the Bayesian MCMC calculates the given CO2 data by a mathematical model based on a statistical approach. The present space model is a single-office room in which the CO2 concentration is determined through several simulation schemes and experiments. The estimation accuracy of the neural network depends on the complexity of the input parameters (i.e., CO2 concentration and ventilation rate), whereas the Bayesian MCMC is influenced by uncertainty in the CO2 concentration. Both methods produce acceptable estimates under certain treatments.

Modelling maximum river flow by using Bayesian Markov Chain Monte Carlo

Analysis of flood trends is vital since flooding threatens human living in terms of financial, environment and security. The data of annual maximum river flows in Sabah were fitted into generalized extreme value (GEV) distribution. Maximum likelihood estimator (MLE) raised naturally when working with GEV distribution. However, previous researches showed that MLE provide unstable results especially in small sample size. In this study, we used different Bayesian Markov Chain Monte Carlo (MCMC) based on Metropolis-Hastings algorithm to estimate GEV parameters. Bayesian MCMC method is a statistical inference which studies the parameter estimation by using posterior distribution based on Bayes’ theorem. Metropolis-Hastings algorithm is used to overcome the high dimensional state space faced in Monte Carlo method. This approach also considers more uncertainty in parameter estimation which then presents a better prediction on maximum river flow in Sabah.

Hybrid larch heterosis: for which traits and under which genetic control?

Despite the interest foresters have for inter-specific hybrid trees, still little is known about their quantitative genetics. This is especially true for the hybrid (HL) between Larix decidua (EL) and L. kaempferi (JL). Long-term, well-designed, multi-site experiments are necessary to estimate the parameters required for HL breeding programs. This paper presents the results from a diallel mating trial between nine EL and nine JL, set up in three contrasted sites. Growth traits (height, circumference), quality traits (wood density, stem form, heartwood proportion), and bud flush were measured from plantation to up to 18 years after plantation. Wood density and heartwood proportion were assessed using increment cores. We did a spatial analysis to take into account environmental heterogeneity at the tree level, and we fitted a multi-trait, Bayesian MCMC (Markov chain Monte Carlo) genetic model. Our study confirmed, in most situations, that HL expressed heterosis over its best parent for growth traits taking advantage of an early faster growth, with no loss in wood density. However, growth traits showed low levels of heritability. On the other hand, bud flush and stem flexuosity had high heritabilities, and wood density was clearly under JL control. Site-dependent heritabilities were expressed by EL. Additive genetic correlations were presented. The traits with high heritabilities showed high correlation between their performances in pure species and in hybridization, as well as high across-site correlations. The discussion focused on the interest of these genetic parameters for the hybrid larch breeding programs.

Simulation evaluation of emerging estimation techniques for multinomial probit models

A simulation evaluation is presented to compare alternative estimation techniques for a five-alternative multinomial probit (MNP) model with random parameters, including cross-sectional and panel datasets and for scenarios with and without correlation among random parameters. The different estimation techniques assessed are: (1) The maximum approximate composite marginal likelihood (MACML) approach; (2) The Geweke-Hajivassiliou-Keane (GHK) simulator with Halton sequences, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; (3) The GHK approach with sparse grid nodes and weights, implemented in conjunction with the composite marginal likelihood (CML) estimation approach; and (4) a Bayesian Markov Chain Monte Carlo (MCMC) approach. In addition, for comparison purposes, the GHK simulator with Halton sequences was implemented in conjunction with the traditional, full information maximum likelihood approach as well. The results indicate that the MACML approach provided the best performance in terms of the accuracy and precision of parameter recovery and estimation time for all data generation settings considered in this study. For panel data settings, the GHK approach with Halton sequences, when combined with the CML approach, provided better performance than when implemented with the full information maximum likelihood approach, albeit not better than the MACML approach. The sparse grid approach did not perform well in recovering the parameters as the dimension of integration increased, particularly so with the panel datasets. The Bayesian MCMC approach performed well in datasets without correlations among random parameters, but exhibited limitations in datasets with correlated parameters.

Coronal loop seismology using damping of standing kink oscillations by mode coupling

Context. The strong damping of kink oscillations of coronal loops can be explained by mode coupling. The damping envelope depends on the transverse density profile of the loop. Observational measurements of the damping envelope have been used to determine the transverse loop structure which is important for understanding other physical processes such as heating. Aims. The general damping envelope describing the mode coupling of kink waves consists of a Gaussian damping regime followed by an exponential damping regime. Recent observational detection of these damping regimes has been employed as a seismological tool. We extend the description of the damping behaviour to account for additional physical effects, namely a time-dependent period of oscillation, the presence of additional longitudinal harmonics, and the decayless regime of standing kink oscillations. Methods. We examine four examples of standing kink oscillations observed by the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO). We use forward modelling of the loop position and investigate the dependence on the model parameters using Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling. Results. Our improvements to the physical model combined with the use of Bayesian inference and MCMC produce improved estimates of model parameters and their uncertainties. Calculation of the Bayes factor also allows us to compare the suitability of different physical models. We also use a new method based on spline interpolation of the zeroes of the oscillation to accurately describe the background trend of the oscillating loop. Conclusions. This powerful and robust method allows for accurate seismology of coronal loops, in particular the transverse density profile, and potentially reveals additional physical effects.

Variational Hamiltonian Monte Carlo via Score Matching.

Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC). In recent years, however, several methods have been proposed based on combining variational Bayesian inference and MCMC simulation in order to improve their overall accuracy and computational efficiency. This marriage of fast evaluation and flexible approximation provides a promising means of designing scalable Bayesian inference methods. In this paper, we explore the possibility of incorporating variational approximation into a state-of-the-art MCMC method, Hamiltonian Monte Carlo (HMC), to reduce the required expensive computation involved in the sampling procedure, which is the bottleneck for many applications of HMC in big data problems. To this end, we exploit the regularity in parameter space to construct a free-form approximation of the target distribution by a fast and flexible surrogate function using an optimized additive model of proper random basis, which can also be viewed as a single-hidden layer feedforward neural network. The surrogate function provides sufficiently accurate approximation while allowing for fast computation in the sampling procedure, resulting in an efficient approximate Bayesian inference algorithm. We demonstrate the advantages of our proposed method using both synthetic and real data problems.

Geographical Analysis of Lung Cancer Mortality Rate and PM2.5 Using Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth

Exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) may increase risk of lung cancer. The repetitive and broad-area coverage of satellites may allow atmospheric remote sensing to offer a unique opportunity to monitor air quality and help fill air pollution data gaps that hinder efforts to study air pollution and protect public health. This geographical study explores if there is an association between PM2.5 and lung cancer mortality rate in the conterminous USA. Lung cancer (ICD-10 codes C34- C34) death count and population at risk by county were extracted for the period from 2001 to 2010 from the U.S. CDC WONDER online database. The 2001-2010 Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset was used to calculate a 10 year average PM2.5 pollution. Exploratory spatial data analyses, spatial regression (a spatial lag and a spatial error model), and spatially extended Bayesian Monte Carlo Markov Chain simulation found that there is a significant positive association between lung cancer mortality rate and PM2.5. The association would justify the need of further toxicological investigation of the biological mechanism of the adverse effect of the PM2.5 pollution on lung cancer. The Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth dataset provides a continuous surface of concentrations of PM2.5 and is a useful data source for environmental health research.

NONPOLIO ENTEROVIRUSES WHICH CAUSED THE RISE OF ENTEROVIRUS INFECTION ON SOME TERRITORIES OF RUSSIA IN 2016

Aim: Characteristics of the peculiarities and the etiological factor of enterovirus infection on some territories of Russia in 2016. Materials and methods: We investigated 2138 samples from the patients with enterovirus infection. The isolation and identification of enteroviruses were conducted by the virological method and by partial sequencing of the genome region VP1. Phylogenic trees were constructed according to the method of Bayesian Monte Carlo Markov Chain. Results: Epidemic peaks of enterovirus infection were fixed on some territories of Russia. In Saratov region the morbidity index of enterovirus infection in 2016 was twice as high as the median morbidity index over previous years. The morbidity level of enterovirus meningitis – 3, 21 for 100000 of the population (77% from all the cases of enterovirus infection) was higher than on the other territories. In Kostroma region the morbidity index of enterovirus infection in 2016 was 11 times higher than the index of the previous year. On both territories the rise of morbidity depends on the active circulation of enterovirus ЕСНО 30. Enteroviruses ECHO30 from Saratov region belonged to two phylogenic groups of genotype h. To one of them belonged viruses ECHO30 from Kostroma region. In Murmansk and Leningrad regions in 2016 most cases of enterovirus infection were represented by hand, foot and mouth disease (HFMD). The grouped foci of infection were registered in some preschool institutions. The etiological factor of this clinical form was Coxsackieviruses A6 belonging to different genetic variants. Conclusion: Epidemic peaks of enterovirus infection with the prevalence of different clinical forms of the disease were provoked by different etiological factors. On territories where enterovirus meningitis prevailed strains of enterovirus ECHO30 belonging to different variants of genotype h were detected. In patients with clinical picture of HFMD from territories where this form was leading the etiological factor of infection was Coxsackievirus A6 of different genetic variants.

Differences in the integrase and reverse transcriptase transmitted resistance patterns in Northern Poland

IntroductionWith the widespread introduction of the integrase (In) inhibitors into clinical practice, transmission of drug resistance to this class of antiretroviral medications may expand. The aim of this study was to analyze the recent patterns of In resistance in treatment naive individuals in Northern Poland and its association with transmitted protease (PR) and reverse transcriptase (RT) mutations. MethodsStudy included 172 PR, RT and InI sequences from antiretroviral treatment naive HIV-1 infected patients linked to care in Northern Poland from 2010 to 2015. Drug resistance was interpreted based on the WHO surveillance and IAS-USA mutation lists. For phylogeny maximum likelihood and Bayesian Monte Carlo Markov Chain analyses were used. ResultsOverall rate of transmitted drug resistance was 12.21%. Nucleoside reverse transcriptase inhibitor (NRTI) resistance associated substitutions were found in 11.05% of cases and non-nucleoside reverse transcriptase inhibitor resistance variants in 1.16%. In multivariate models transmitted resistance strongly associated with subtype D infections [66.67% compared to the 3.84% for subtype B (p=0.001)]. No transmission of major protease or integrase mutations were observed. Polymorphisms associated with resistance against integrase inhibitor, mostly E157Q, were found in 21.5% sequences and associated with female (31.91% vs. 15.2% for male, p=0.01), injection drug use (84.21% compared to 22.08% for heterosexual and 1.39% for men-who-have-sex-with-men transmissions, p<0.0001) as well as hepatitis C coinfection [63.64% for positive, versus 8.57% for HCV antibody negative, p<0.0001]. Clusters of nucleoside reverse transcriptase mutations in subtype D and integrase E157Q variants in subtype B were observed. ConclusionsTransmitted drug resistance frequency was high in subtype D but limited to clustered NRTI mutations, being infrequent among subtype B infected cases. Despite lack of major integrase resistance in treatment naive patients, variants potentially affecting susceptibility to this class were common, which indicates the potential need for extended surveillance in the near future.

The southern Sierra Nevada pediment, central California

The southern Sierra Nevada foothills, central California (USA), expose a fossil pre–40 Ma bedrock pediment which we call the southern Sierra Nevada pediment. We document this landscape with multiple types of data, and also report new apatite ^4He/^3He, (U-Th)/He, and zircon (U-Th)/He data from the pediment that significantly expand the spatial extent of southern Sierra low-temperature thermochronology data westward into the foothills. Applying recently published thermal modeling software for thermochronologic data, which uses a transdimensional Bayesian Monte Carlo Markov chain statistical approach, we tightly constrain the thermal history of the southern Sierra Nevada pediment. Integrating this thermal history with numerous previously published data sets from across the southern Sierra, we present a chronology of tectonic and landscape evolution of the southern Sierra Nevada. For the first time we cover the entire width of the range, integrate the numerous published data sets into a single coherent geologic story, and link each phase of this story to a potential mechanism. Modeling results are consistent with a three-phase cooling history for the southern Sierra Nevada pediment. Rapid exhumation ca. 95–85 Ma resulted in cooling to between 55 °C and 100 °C. Following this, slow cooling to surface conditions occurred from 85 Ma to 40 Ma at rates consistent with those estimated for the axial southern Sierra during the same time period by previous studies. Little if any additional cooling occurred post–40 Ma. We hypothesize that a thin sedimentary cover protected the 40 Ma bedrock landscape through much of the last 40 m.y., and that this cover eroded away post–10 Ma, re-exhuming the southern Sierra Nevada pediment as a fossil pre–40 Ma landscape. Each of these three phases of cooling links to a distinct tectonic or geomorphic regime, including the profound rapid exhumation of the southern Sierra Nevada–Mojave segment of the Cretaceous arc due to subduction of a large oceanic plateau, the formation of the low-relief landscape of the high-elevation areas of the southern Sierra Nevada with more limited tectonic forcing, and Eocene activity on the Western Sierra Fault System.

Gaussian process emulators for spatial individual‐level models of infectious disease

AbstractStatistical inference for spatial models of infectious disease spread is often very computationally expensive. These models are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework, which requires multiple iterations of the computationally cumbersome likelihood function. We here propose a method of inference based on so‐called emulation techniques. Once again the method is set in a Bayesian MCMC context, but avoids calculation of the computationally expensive likelihood function by replacing it with a Gaussian process approximation of the likelihood function built from simulated data. We show that such a method can be used to infer the model parameters and underlying characteristics of the spatial disease system, and this can be done in a computationally efficient manner. The Canadian Journal of Statistics 44: 480–501; 2016 © 2016 Statistical Society of Canada

The influence of non-stationarity in extreme hydrological events on flood frequency estimation

AbstractSubstantial evidence shows that the frequency of hydrological extremes has been changing and is likely to continue to change in the near future. Non-stationary models for flood frequency analyses are one method of accounting for these changes in estimating design values. The objective of the present study is to compare four models in terms of goodness of fit, their uncertainties, the parameter estimation methods and the implications for estimating flood quantiles. Stationary and non-stationary models using the GEV distribution were considered, with parameters dependent on time and on annual precipitation. Furthermore, in order to study the influence of the parameter estimation approach on the results, the maximum likelihood (MLE) and Bayesian Monte Carlo Markov chain (MCMC) methods were compared. The methods were tested for two gauging stations in Slovenia that exhibit significantly increasing trends in annual maximum (AM) discharge series. The comparison of the models suggests that the stationary model tends to underestimate flood quantiles relative to the non-stationary models in recent years. The model with annual precipitation as a covariate exhibits the best goodness-of-fit performance. For a 10% increase in annual precipitation, the 10-year flood increases by 8%. Use of the model for design purposes requires scenarios of future annual precipitation. It is argued that these may be obtained more reliably than scenarios of extreme event precipitation which makes the proposed model more practically useful than alternative models.

Spatial GR4J conceptualization of the Tamor glaciated alpine catchment in Eastern Nepal: evaluation of GR4JSG against streamflow and MODIS snow extent

AbstractSnow and glacial melt processes are an important part of the Himalayan water balance. Correct quantification of melt runoff processes is necessary to understand the region's vulnerability to climate change. This paper describes in detail an application of conceptual GR4J hydrological model in the Tamor catchment in Eastern Nepal using typical elevation band and degree‐day factor approaches to model Himalayan snow and glacial melt processes. The model aims to provide a simple model that meets most water planning applications. The paper contributes a model conceptualization (GR4JSG) that enables coarse evaluation of modelled snow extents against remotely sensed Moderate Resolution Imaging Spectroradiometer snow extent. Novel aspects include the glacial store in GR4JSG and examination of how the parameters controlling snow and glacial stores correlate with existing parameters of GR4J. The model is calibrated using a Bayesian Monte Carlo Markov Chain method against observed streamflow for one glaciated catchment with reliable data. Evaluation of the modelled streamflow with observed streamflow gave Nash Sutcliffe Efficiency of 0.88 and Percent Bias of &lt;4%. Comparison of the modelled snow extents with Moderate Resolution Imaging Spectroradiometer gaveR2of 0.46, with calibration against streamflow only. The contribution of melt runoff to total discharge from the catchment is 14–16% across different experiments. The model is highly sensitive to rainfall and temperature data, which suffer from known problems and biases, for example because of stations being located predominantly in valleys and at lower elevations. Testing of the model in other Himalayan catchments may reveal additional limitations. © 2016 The Authors.Hydrological Processespublished by John Wiley &amp; Sons Ltd.