Estimated Parameter Values Research Articles

Relationships between emotional labor, job burnout, and emotional intelligence: an analysis combining meta-analysis and structural equation modeling

AbstractThe present study adopted a meta-analysis design that incorporated structural equation modeling to explore the relationships between emotional labor (EL), job burnout (JB), and emotional intelligence (EI), and enable model validation. The results revealed that EL and JB were significantly and positively correlated, that EI was significantly and positively correlated with EL, and that EI was significantly and negatively correlated with JB. The SEM parameter estimation values were all positive, reaching the level of significance and meeting the basic fit criteria. The total effect size of EL on JB was 0.289, which was equal to the sum of the direct and indirect effect sizes (0.371–0.082). This result indicated that EL affected JB through EI, validating the presence of a moderating effect. Finally, the results were discussed, and practical suggestions were proposed.

Integrated Subsurface Hydrologic Modeling for Agricultural Management Using HYDRUS and UZF Package Coupled with MODFLOW

The present work aims to compare two different subsurface hydrological models, namely HYDRUS and MODFLOW UZF package, in terms of groundwater recharge; thus, both models were coupled with MODFLOW. The study area is an experimental kiwifruit orchard located in the Arta plain in the Epirus region of Greece. A novel conceptual framework is introduced in order to (i) use in situ and laboratory measurements to estimate parameter values for both sub-surface flow models; (ii) couple the developed models with MODFLOW to estimate groundwater recharge; and (iii) compare and evaluate the performance of both approaches, with differences stemming from the distinctive equations describing the flow in the unsaturated zone. Detailed soil investigation was conducted in two soil horizons in the research field to identify soil texture zones, along with infiltration experiments implementing both double-ring and single-ring infiltrometers. The results of the field measurements indicate that fine-textured soils are predominant within the field, affecting several hydrological processes, such as infiltration, drainage, and root water uptake. Field measurements were incorporated in unsaturated zone flow modeling and the infiltration fluxes were simulated with the application of both the UZF package of MODFLOW and the HYDRUS code. The two codes presented acceptable agreement between the simulated and observed hydraulic head values with a similar performance in terms of statistics; however, they produced different results regarding recharge rates in the aquifer as simulated by MODFLOW. HYDRUS produced higher hydraulic head values in the aquifer throughout the simulation, related to higher recharge rates arising from the root water uptake and the capillary effects that are computed by HYDRUS but neglected by the UZF package of MODFLOW.

Generating spatially realistic environmental null models with the shift‐&‐rotate approach helps evaluate false positives in species distribution modelling

Abstract To circumvent reporting spurious correlations, species distribution models often explicitly account for spatial autocorrelation, for example by including spatially structured random effects. The validity of statistical inference derived from such models has been tested by simulations using null environmental predictors that do not have any causal dependency with the response. Such null environmental predictors can be obtained by permutations of the original predictors or by simulating spatial structures resembling the original predictors. In such approaches, it is important that the permuted or simulated predictors reflect the nature of spatial variation present in the original predictors. Here we present a novel approach for generating realistic null predictors by a shift‐&‐rotate (S&R) approach: we extract environmental variables after randomly translating and rotating the sampling area within a window of defined environmental layers. In this way, the null environmental variables have fully realistic spatial variation and covariation, but no relationship to the response variable. We implement the S&R approach to three main R‐functions and demonstrate with a simulation study how they can be used to untangle causal versus non‐causal relationships within species distribution modelling. These methods allow us to quantify the predictive power attributed within the models due to non‐causal correlations generated by the realistic structure of the environmental covariates. In our case study, we identify when a model incorrectly estimates parameter values, yet still has high predictive power due to the structured nature of the predictor variables. The use of null models is imperative in ecological modelling for testing the accuracy of statistical inference in complex ecological systems and the choice of these null models is far from trivial. Here we provide R functions for generating spatially realistic null models to use in species distribution modelling as well as other spatially explicit fields such as landscape genetics.

Comparative analysis of event runoff coefficients and curve numbers in contrasting urban environments based on observed rainfall-runoff data

Recently, there has been a growing recognition that the generic values of runoff coefficient (C) and curve number (CN) from standard lookup tables in the literature may not accurately capture the specific characteristics of a given catchment. This study aims to estimate and compare the event C and CN of two contrasting urban catchments in Ljubljana, Slovenia using observed rainfall-runoff data. Estimated parameter values were compared to the tabulated values from the literature (i.e., ASCE, 1992; USDA-NRCS, 2004). Seasonal changes of C and CN, along with diurnal streamflow patterns, were also analyzed to understand the eco-hydrological processes in the urban forest. The results demonstrated that the urban mixed forest exhibited high variability of Cs across all rainfall events with a mean and median of 0.11 and 0.062, respectively. Most of the C values observed in the urban area are clustered around the central tendency with a mean and median of 0.60. These mean C values in both catchments were lower than the tabulated values in the ASCE (1992) design manual. Mean and median CN values in the urban area were 95.45 and 96.81, respectively, lower than the urban mixed forest’s CN values of 82.69 and 83.95. An asymptotic CN∞ of 90.69 was found for the urban area and 71.71 for the urban mixed forest. Central tendency-derived CN values tend to be slightly higher than the tabulated values from USDA-NRCS (2004) and gridded values from GCN250 (Jaafar et al., 2019), while asymptotic CN∞ values were lower. Using central tendency measures as the single lumped value of C and CN may provide a reasonable representation of the runoff behavior in the studied urban area. However, this may present certain challenges in the urban forest catchment. Additionally, pre-event soil moisture conditions and specific storm characteristics contributed to the observed variations in C and CN. Bi-monthly analysis showed that C and CN were high during the autumn and winter months. Diurnal streamflow pattern is most prevalent during low-flow and precipitation-free periods while exhibiting seasonal structures in terms of the shape and timing of maxima and minima. Local estimation of C and CN allows for a more tailored representation of the catchment’s hydrological behavior.

Facial basis of stereotypes: Judgements of warmth and competence based on cross-group typicality/distinctiveness of faces.

Human migration is an increasingly common phenomenon and migrants are at risk of disadvantageous treatment. We reasoned that migrants may receive differential treatment by locals based on the closeness of their facial features to the host average. Residents of Türkiye, the country with the largest number of refugees currently, served as participants. Because many of these refugees are of Arabic origin, we created target facial stimuli varying along the axis connecting Turkish and Arabic morphological prototypes (excluding skin colour) computed using geometric morphometrics and available databases. Participants made judgements of two universal dimensions of social perception-warmth and competence-on these faces. We predicted that participants judging faces manipulated towards the Turkish average would provide higher warmth and competence ratings compared to judging the same faces manipulated towards the Arabic average. Bayesian statistical tools were employed to estimate parameter values in multilevel models with intercorrelated varying effects. The findings did not support the prediction and revealed raters (as well as target faces) to be an important source of variation in social judgements. In the absence of simple cues (e.g. skin colour, group labels), the effect of facial morphology on social judgements may be much more complex than previously assumed.

Bayesian analyses of multiple random change points in survival models with applications to clinical trials

ABSTRACT Single and multiple random change points (RCPs) in survival analysis have arisen naturally in oncology trials, where the time to hazard rate change differs from one subject to another. Recently, Xu formulated and discovered important properties of these survival models using a frequentist approach, allowing us to estimate the hazard rates, rate parameters of the exponential distributions for the RCPs, expected survival and hazard functions. However, these methods did not provide an estimation of the uncertainty or the confidence intervals for the parameters and their differences or ratios. Therefore, statistical inferences were not able to be drawn on the parameters and their comparisons. To solve this issue, this article implements a Gibbs sampler method to estimate the above parameters and the differences or ratios alongside the 100(1 − α )% highest posterior density (HPD) intervals calculated from Chen–Shao’s algorithm. The estimated rate parameters from the methods in Xu serve as empirical values in the Gibbs sampler method. Thus, formal statistical inferences can now be readily drawn. Simulation studies demonstrate that the proposed methods yield robust estimates, with the samples from the marginal posterior distributions converging rapidly and exhibiting favorable behavior. The 95% HPD intervals also demonstrate excellent coverage probabilities. This proposed method has a multitude of applications in clinical trials such as efficient clinical trial design and sample size adjustment based on the estimated parameter values at interim analyses.

Estimating the Values of the PDE Model Parameters of Rechargeable Lithium-Metal Battery Cells using Linear EIS

Abstract We introduce a partial-differential-equation model for rechargeable lithium-metal battery (LMB) cells whose parameter values are fully identifiable from cell-level experiments. From this model, we formulate a computationally tractable transfer-function (TF) model for use within optimization loops. A strategy is proposed for regressing the TF model to cell electrochemical impedance spectroscopy (EIS) measurements to estimate parameter values. We validate the regression using a synthetic dataset before application to a single-layer LMB pouch cell. The voltage RMSE between the fully identified model's predictions and laboratory measurements is about 4 mV for a GITT profile. We provide MATLAB code to simulate the model in COMSOL, compute cell impedance from the TF model, and perform model regression.

Integrating the Finite Element Method with Python Scripting to Assess Mining Impacts on Surface Deformations

Mining operations disrupt the structure of rock layers, leading to surface deformations and potential mining damage. This issue has been extensively studied since the 19th century using various analytical, geometric-integral, and stochastic methods. Since the 1990s, numerical methods have been increasingly applied to determine changes in the stress and strain states of rock masses due to mining activities. These methods account for numerous additional factors influencing surface deformation, offering significant advantages over classical approaches. However, modelling rock masses presents challenges, particularly in calibrating the mechanical parameters of rock layers, an area extensively researched with numerous publications. In this study, we determined the mechanical parameter values of rock layers at the advancing mining front using a custom Python script and Finite Element Method (FEM) numerical models. We also introduced a modification to evaluate the error of the estimated parameter values. Numerical analyses were conducted for the Piast–Ziemowit mine region in Poland, utilizing mining, geological, and surveying data. Our results demonstrate that accurate calibration of mechanical parameters is crucial for reliable predictions of surface deformations. The proposed methodology enhances the precision of numerical models, providing a more robust framework for assessing the impact of mining activities on rock layers.

Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference

In analytical modeling for biochemical pathways, precisely determining unknown parameters is paramount. Traditional methods, reliant on experimental time course data, often encounter roadblocks — limited accessibility and variable quality — that can significantly impact the algorithm’s performance. In this study, we address these hurdles by unveiling a groundbreaking parameter estimation technique, Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF). This innovative approach eliminates the need for experimental time-course measurements and capitalizes on the existing imprecise relationships among the molecules within the network. Our proposed framework integrates a Fuzzy Inference System (FIS) block to encapsulate these approximated relationships. To fine-tune the estimated parameter values, we employ Tikhonov regularization. The selection of Tikhonov regularization and Gaussian membership functions was based on the Mean Squared Error (MSE) values observed during the parameter estimation process, contrasting our results with those of previous studies.We rigorously tested the proposed approach across various pathways, from the glycolytic processes in mammalian erythrocytes and yeast cells to the intricate JAK/STAT and Ras signaling pathways. The results were impressive, showing a significant similarity (p-value < 0.001) to the outcomes of specific prior experiments. The dynamics of the biochemical networks normalized within the [0, 1] range mirrored the transient behavior (MSE < 0.5) of both in vivo and in silico results from previous studies. In conclusion, our findings highlight the effectiveness of CRFIEKF in estimating the kinetic parameter values without prior knowledge of experimental data within a biochemical pathway in the state-space model. The proposed method underscores its potential as a game-changer in biochemical pathway analysis.

Distributed control of spacecraft formation under [formula omitted] perturbation in the port-Hamiltonian framework

To control the relative position and relative velocity of spacecraft formation, a time-varying nonlinear relative motion model under J2 perturbation in an elliptical orbit is established in the port-Hamiltonian (PH) framework, and a distributed control law for spacecraft formation is developed using the consensus algorithm for parameter estimation and the passivity-based control (PBC) method based on the state-error interconnection and damping assignment (IDA) technique. First, the influence of J2 perturbation on the potential energy and orbit parameters in the model is considered when the relative motion model is built in the PH frame, and the expression of the relative motion acceleration under J2 perturbation is given. Second, to solve the problem that the state of the chief spacecraft cannot be directly obtained from the deputy spacecraft under distributed communication, a consensus-based parameter estimation method is introduced, the estimated parameter of the chief spacecraft is applied to the relative motion model in the PH frame, and a state error model with the estimated parameters derived from the consistency algorithm is established. Then, after the stability analysis is conducted on the desired PH system containing the estimated parameter values, the desired Hamiltonian energy function with the estimated parameters is designed according to the time-varying errors of the relative equilibrium states of the deputy spacecraft and the errors between deputy spacecraft, and the distributed control law of spacecraft formation is derived based on the state-error IDA-PBC method. Finally, the expected relative motion trajectory designed based on the TH equation is used to simulate a spacecraft formation under J2 perturbation, and the results indicate that the spacecraft can quickly converge to the expected formation under the influence of the control law.

Validation and psychometric evaluation of the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) among Czech adolescents using Item Response Theory

BackgroundThe topic of adolescent mental health is currently a subject of much debate due to the increasing prevalence of mental health problems among this age group. Therefore, it is crucial to have high-quality and validated mental well-being measurement tools. While such tools do exist, they are often not tailored specifically to adolescents and are not available in Czech language. The aim of this study is to validate and test the Czech version of the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) on a large sample of Czech adolescents aged 15 to 18 years.MethodsThe analysis is based on data from the first wave of the Czech Education Panel Survey (CZEPS) and was mainly conducted using Item Response Theory (IRT), which is the most appropriate method for this type of analysis. Specifically, the Graded Response Model (GRM) was applied to the data. This comprehensive validation study also included reliability and three types of validity (construct, convergent and criterion) testing.ResultsThe study found that the Czech version of the SWEMWBS for adolescents aged 15 to 18 years (N = 22,498) has good quality and psychometric properties. The data was analysed using the GRM model as it met the assumptions for the use of IRT. The estimated parameter values by GRM demonstrated good discriminant and informative power for all items, except for item 7, which showed poorer results compared to the others. However, excluding it from the scale would not enhance the overall quality of the scale. The five-category response scale functions effectively. Additionally, the results demonstrated high reliability, and all types of validity tested were also confirmed.ConclusionsThe Czech version of the SWEMWBS for adolescents has been validated as a psychometrically sound, reliable and valid instrument for measuring mental well-being. It can therefore be used with confidence in future studies.

Heterogeneous Models for UV Induced Precursor-Mediated Growth of CdS Nanoparticles in PMMA Films: Fitting Experimental Curves and Core-Shell Growth.

Experimental data on the absorbance evolution of an UV irradiated PMMA film containing the TEDBCd CdS precursor published earlier are fitted by the authors' recently published model of precursor-mediated heterogeneous nucleation and growth of nanoparticles in polymer films. Two types of nucleation centers are considered. The centers of the first type are nanoparticles initially existing in the material. The centers of the second type belong to polymer chains. The study shows that the experimental data indicate a wide size distribution of the obtained CdS nanoparticles. For the estimated parameter values, the problem of core-shell nanoparticle growth where the nucleation centers of the first type are nanoparticles of a different nature is considered.

Dynamic modeling of lytic virus transmission among phytoplankton driven by nitrogen and phosphorus

Lytic viruses infect phytoplankton and cause their mass mortalities. A dynamic model is proposed to explore lytic virus transmission among phytoplankton. The ecological reproductive index for the survival ability of phytoplankton and the basic reproduction number for the spreading ability of lytic viruses are established by analyzing dynamical properties of the model. We estimate parameter values of the model from Maat and Brussaard’s experimental data and theoretically examine Maat and Brussaard’s hypotheses that lytic virus transmission is driven by nitrogen and phosphorus. Our studies show that lytic viruses cannot spread among phytoplankton in low nitrogen or low phosphorus aquatic environments. The numerical diagrams also suggest that the increase in nitrogen and phosphorus during the spread of lytic viruses does not always cause phytoplankton blooms.

Dengue dynamics in Nepal: A Caputo fractional model with optimal control strategies

An infectious disease called dengue is a significant health concern nowadays. The dengue outbreak occurred with a single serotype all over Nepal in 2023. In the tropical and subtropical regions, dengue fever is a leading cause of sickness and death. Currently, there is no specified treatment for dengue fever. Avoiding mosquito bites is strongly advised to reduce the likelihood of controlling this disease. In underdeveloped countries like Nepal, the implementation of appropriate control measures is the most important factor in preventing and controlling the spread of dengue illness. The Caputo fractional dengue model with optimum control variables, including mosquito repellent and insecticide use, investigates the impact of alternative control strategies to minimize dengue prevalence. Using the fixed point theorem, the existence and uniqueness of a solution will be demonstrated for the problem. Ulam-Hyers stability, disease-free equilibrium point stability, and basic reproduction number are studied for the proposed model. The model is simulated using a two-step Lagrange interpolation technique, and the least squares method is used to estimate parameter values using real monthly infected data. We then analyze the sensitivity analysis to determine influencing parameters and the control measure effects on the basic reproduction number. The Pontryagin Maximum Principle is used to determine the optimal control variable in the dengue model for control strategies. The present study suggests that the deployment of control measures is extremely successful in lowering infectious disease incidences. Which facilitates the decision-makers to practice rigorous evaluation of such an epidemiological scenario while implementing appropriate control measures to prevent dengue disease transmission in Nepal.

Log-Linear Model Analysis of Aftershock Sequences: A Review on the 6 February Earthquakes in Turkey

Researchers have conducted numerous studies on earthquakes and aftershocks, some of which have utilized statistical analysis methods. However, there is no direct research examining the interaction between variables thought to influence aftershocks following major earthquakes. In this study, 2194 aftershocks with a magnitude of 3 or higher that occurred after two major earthquakes in Turkey on February 6, 2023 were analyzed using log-linear models with respect to variables such as depth, magnitude, time, and city. At the end of the study, all four primary variables - city, magnitude, depth, and time - were found to be statistically significant. Based on the parameter estimation values, it was found that the probability of aftershocks occurring in Malatya was 1.17 times greater than in Adıyaman, 2.82 times greater than in Gaziantep, and 1.38 times greater than in Hatay, while the probability of aftershocks occurring in Kahramanmaraş was 3 times greater than in Malatya. Thus, it can be said that the aftershocks are influenced by the center of the major earthquake. Additionally, it was found that the probability of aftershocks with a magnitude between 3 and 3.5 was 1.4 times greater than those with a magnitude of 4 or higher, and the probability of aftershocks with a depth of less than 10 kilometers was 2 times greater. We believe that the results of this study will provide information on aftershocks that occur after major earthquakes and will be helpful for future studies.

Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Estimating the Susceptible-Exposed-Infected-Recovered (SEIR) Model Parameter Values

Background: The most commonly used mathematical model for analyzing disease spread is the Susceptible-Exposed-Infected-Recovered (SEIR) model. Moreover, the dynamics of the SEIR model depend on several factors, such as the parameter values. Objective: This study aimed to compare two optimization methods, namely genetic algorithm (GA) and particle swarm optimization (PSO), in estimating the SEIR model parameter values, such as the infection, transition, recovery, and death rates. Methods: GA and PSO algorithms were compared to estimate parameter values of the SEIR model. The fitness value was calculated from the error between the actual data of cumulative positive COVID-19 cases and the numerical data of cases from the solution of the SEIR COVID-19 model. Furthermore, the numerical solution of the COVID-19 model was calculated using the fourth-order Runge-Kutta algorithm (RK-4), while the actual data were obtained from the cumulative dataset of positive COVID-19 cases in the province of Jakarta, Indonesia. Two datasets were then used to compare the success of each algorithm, namely, Dataset 1, representing the initial interval for the spread of COVID-19, and Dataset 2, representing an interval where there was a high increase in COVID-19 cases. Results: Four parameters were estimated, namely the infection rate, transition rate, recovery rate, and death rate, due to disease. In Dataset 1, the smallest error of GA method, namely 8.9%, occurred when the value of , while the numerical error of PSO was 7.5%. In Dataset 2, the smallest error of GA method, namely 31.21%, occurred when , while the numerical error of PSO was 3.46%. Conclusion: Based on the parameter estimation results for Datasets 1 and 2, PSO had better fitting results than GA. This showed PSO was more robust to the provided datasets and could better adapt to the trends of the COVID-19 epidemic. Keywords: Genetic algorithm, Particle swarm optimization, SEIR model, COVID-19, Parameter estimation.

Design Parameter Values for Planning Mediation Studies with Teacher and Student Mathematics Outcomes

Despite the widespread use of mediation analyses to test and unpack program effects across areas of education research, scant empirical literature is available to guide the effective and efficient prospective design of mediation studies. We contribute to this gap by developing an initial collection of empirical estimates of design parameter values for mediation studies drawing on teacher mediators and student outcomes within a three-level structure context: students nested in teachers nested in schools. Our analyses significantly expanded the scope and breadth of prior work by investigating design parameter values across a broad range of contexts (e.g., academic year, summer programs), modes (e.g., surveys, observations, assessments), mediator types (e.g., knowledge, instruction, affect, beliefs, perceptions, attitudes, climate, program implementation), instruments (e.g., MKT, CLASS, MQI, CLE, TIPS), and domains (e.g., algebra, geometry, classroom management). The results demonstrate substantial variability in parameter values across mediators and outcomes and highlight the potential value of collecting pretreatment assessments of mediators (and outcomes) in increasing statistical power. Collectively, the results underscore the significance of drawing on mediator- and outcome-specific design parameter estimates when planning studies.

A mechanistic model to predict headspace concentration profile of carvacrol for controlled release packaging

The efficacy of an antimicrobial agent in food packaging depends largely on its concentration profile in the package headspace. In this study, a mechanistic mathematical model was developed to predict the headspace concentration profile of carvacrol, a prominent antimicrobial agent, using the physical principles that govern the movement of carvacrol molecules in a controlled release packaging system. The model and its estimated parameter values were validated using three different sets of gas chromatography measurements—this rigorous process confirms the accuracy and reliability of the model. Advancing beyond existing models, our model enhances the understanding of the dynamics between the concentration profile and its interacting processes including evaporation, adsorption, and gas transmission. It provides packaging developers with a superior alternative to the trial-and-error approach to tailor concentration profiles for optimizing antimicrobial efficacy. It leads to more efficient and better design of controlled release packaging.

Segmentation of stunting, wasting, and underweight in Southeast Sulawesi using geographically weighted multivariate Poisson regression

The health profile of Southeast Sulawesi Province in 2021 shows that the prevalence of stunting is 11.69 %, wasting 5.89 % and underweight 7.67 %. This relatively high figure should be immediately reduced to zero because it greatly affects the quality of human resources. Cases of stunting, wasting and underweight are an iceberg phenomenon, especially in Southeast Sulawesi. Therefore, it is necessary to research the number of cases of stunting, wasting and underweight in Southeast Sulawesi using GWMPR. The research results show that there is a trivariate correlation between the number of cases of stunting, wasting and underweight. The GWMPR model provides better results in modeling the number of stunting, wasting and underweight cases than the MPR model. The models produced for each sub-district are different from each other based on the predictor variables that have a significant effect and the estimated parameter values ​​for each sub-district. The segmentation of the number of stunting cases consists of 21 regional groups with 10 significant predictor variables, while the number of wasting cases consists of 10 regional groups with 9 significant predictor variables, while the number of underweight cases consists of 37 regional groups with 11 significant predictor variables. Therefore, policies on stunting, wasting, and underweight should be based on local conditions. 3 important components of this study: 1. GWMPR is the development of GWPR model when there are 2 or more response variables that are correlated. 2. GWMPR is a spatial model that considers geography. 3. Application of GWMPR to the analysis of the number of stunting, wasting, and underweight in Southeast Sulawesi province.

Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo.

On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.