Tools for analysing additive manufacturability often employ complex models that lack transparency; this impedes user understanding and has detrimental effects on the implementation of results. An expert system tool that transparently learns features for successful printing has been created. The tool uses accessible data from STL models and printer configurations to create explainable parameters and identify risks. Testing has shown good agreement to print behaviour and easy adaptability. The tool reduces the learning curves designers face in understanding design for additive manufacturing.

The dual language development of dual language immersion (DLI) students, although often examined at the domain level (e.g., listening or reading), remains understudied for more specific skills (e.g., word, sentence, or discourse). This study examines the eleven-month progression of oral language skills in a picture description task in two languages (French and English) for early-elementary (Transitional Kindergarten through first grade) DLI students (N = 42). Using Bayesian methods, which estimate parameters using both the data and prior information, we describe French and English growth patterns as measured by learning progressions whose focus is on language features at the word, sentence, and discourse levels. For French oral language, we found evidence of meaningful positive linear growth for all language features, whereas for English oral language, meaningful linear positive growth was only detected for sophistication of topic vocabulary. Overall, coming from a French-speaking household was associated with steeper French oral language trajectories, but coming from an English-only household did not specifically impact English oral language trajectories. In both languages, grade level influenced the trajectories of some—but not all—features. We conclude with theoretical and practical implications, advocating for a language progression approach in instruction and research on bilingualism.

Despite their increasing popularity, n-of-1 designs employ data analyses that might not be as complete and powerful as they could be. Borrowing from existing advances in educational and psychological research, this article presents a few techniques and references for rigorous data analytic techniques in n-of-1 research.

Does household debt affect the size of the fiscal multiplier? We investigate the effects of household debt on government spending multipliers using a smooth transition vector autoregression model. Through generalized impulse response functions, we measure whether the effect of government spending on GDP is conditioned by different levels of household debt in Australia, Sweden, and Norway, three countries with high levels of household indebtedness, and in the world’s seven largest economies. Our results indicate that the short-term effects of government spending tend to be higher if fiscal expansion takes place during periods of low household debt. On average, the fiscal multiplier (on impact) is 0.70, 0.61, and 0.79 (percent of GDP) larger when the increase in government spending takes place during periods of low household debt for Australia, Norway, and the United States.

Understanding historical environmental determinants associated with the risk of elevated marine water contamination could enhance monitoring marine beaches in a Canadian setting, which can also inform predictive marine water quality models and ongoing climate change preparedness efforts. This study aimed to assess the combination of environmental factors that best predicts Escherichia coli (E. coli) concentration at public beaches in Metro Vancouver, British Columbia, by combining the region’s microbial water quality data and publicly available environmental data from 2013 to 2021. We developed a Bayesian log-normal mixed-effects regression model to evaluate predictors of geometric E. coli concentrations at 15 beaches in the Metro Vancouver Region. We identified that higher levels of geometric mean E. coli levels were predicted by higher previous sample day E. coli concentrations, higher rainfall in the preceding 48 h, and higher 24-h average air temperature at the median or higher levels of the 24-h mean ultraviolet (UV) index. In contrast, higher levels of mean salinity were predicted to result in lower levels of E. coli. Finally, we determined that the average effects of the predictors varied highly by beach. Our findings could form the basis for building real-time predictive marine water quality models to enable more timely beach management decision-making.

Archaeologists keep a limited arsenal of methods for dating stone features at alpine sites. Radiocarbon (14C) dating is rarely possible, and it is common that dates do not accurately represent the activity of interest (stone feature construction). In this paper I review a legacy set of 89 14C dates for stone driveline sites built by hunter-gatherers in Colorado’s Southern Rocky Mountains. I amend the sample of dates using chronometric hygiene and focus on dates with direct association to hunting features. I then present a newly calibrated set of 29 lichenometric dates for rock features at these sites and use hygiene protocols to remove inaccurate dates. Size-frequency lichenometry, though poorly known in archaeology, provides a way to date stone features indirectly by measuring the growth of long-lived lichens that colonize rock surfaces after construction events. Bayesian modeling of the combined set of dates suggests that the tradition of alpine game driving spans over 6000 years BP, with abundant use over the last 2000 years. Archaeologists must use multiple methods for dating stone features in alpine environments. This Bayesian analysis is a formal effort to combine lichenometry and 14C dating for archaeological interpretation.

Deciding whether or not eradication of an invasive species has been successful is one of the main dilemmas facing managers of eradication programmes. When the species is no longer being detected, a decision must be made about when to stop the eradication programme and declare success. In practice, this decision is usually based on ad hoc rules, which may be inefficient. Since surveillance undertaken to confirm species absence is imperfect, any declaration of eradication success must consider the risk and the consequences of being wrong. If surveillance is insufficient, then eradication may be falsely declared (a Type I error), whereas continuation of surveillance when eradication has already occurred wastes resources (a Type II error). We review the various methods that have been developed for quantifying these errors and incorporating them into the decision-making process. We conclude with an overview of future developments likely to improve the practice of determining invasive species eradication success.

True and Error Theory (TET) is a modern latent variable modeling approach for analyzing sets of preferences held by people. Individual True and Error Theory (iTET) allows researchers to estimate the proportion of the time an individual truly holds a particular underlying set of preferences without assuming complete response independence in a repeated measures experimental design. iTET is thus suitable for investigating research questions such as whether an individual ever is truly intransitive in their preferences (i.e., they prefer a to b, b to c, and c to a). While current iTET analysis methods provide the means of investigating such questions they require a lot of data to achieve satisfactory power for hypothesis tests of interest. This paper overviews the performance and shortcomings of the current analysis methods in efficiently using data, while providing new analysis methods that offer substantial gains in power and efficiency.

Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.

Bayesian statistics offers a normative description for how a person should combine their original beliefs (i.e., their priors) in light of new evidence (i.e., the likelihood). Previous research suggests that people tend to under-weight both their prior (base rate neglect) and the likelihood (conservatism), although this varies by individual and situation. Yet this work generally elicits people’s knowledge as single point estimates (e.g., x has a 5% probability of occurring) rather than as a full distribution. Here we demonstrate the utility of eliciting and fitting full distributions when studying these questions. Across three experiments, we found substantial variation in the extent to which people showed base rate neglect and conservatism, which our method allowed us to measure for the first time simultaneously at the level of the individual. While most people tended to disregard the base rate, they did so less when the prior was made explicit. Although many individuals were conservative, there was no apparent systematic relationship between base rate neglect and conservatism within each individual. We suggest that this method shows great potential for studying human probabilistic reasoning.

The paper investigates the validity of individual perceptions of heart disease risks, and examines how information and risk perceptions affect marginal willingness to pay (MWTP) to reduce risk, using data from a stated preference survey. Results indicate that risk perceptions held before receiving risk information are plausibly related to objective risk factors and reflect individual-specific information not found in aggregate measures of objective risk. After receiving information, individuals’ updates of prior risk assessments are broadly consistent with Bayesian learning. Perceived heart disease risks thus satisfy construct validity and provide a valid basis for inferring MWTP to reduce risk. Consistent estimators of the relationship of MWTP to endogenously perceived risk are developed. Estimating MWTP based on objective rather than subjective risks causes misleading inferences about benefits of risk reduction. An empirical case study shows that estimated benefits may be as much as 60–98 % higher when estimated using individuals’ heterogeneous perceptions of risk than when using aggregate estimates of objective risk. The main contributions include assessing the validity of risk perceptions and their updating, consistently estimating the relationship between MWTP and endogenously perceived risk, and demonstrating the importance of employing risk perception information for accurate benefit measurement.

Mappings play an important role in environmental science applications by allowing practitioners to monitor changes at national and global scales. Over the last decade, it has become increasingly popular to use satellite imagery data and machine learning techniques (MLTs) to construct such maps. Given the black-box nature of many of these MLTs though, quantifying uncertainty in these maps often relies on sampling reference data under stricter conditions. However, practical constraints can sampling such data expensive, which forces stakeholders to make a trade-off between the degree of uncertainty in predictions and the costs of collecting appropriately sampled reference data. Furthermore, quantifying any trade-off is often difficult, as it will depend on many interdependent factors that cannot be fully understood until more data is collected. This paper investigates how a combination of Bayesian inference and an adaptive approach to sampling reference data can offer a generalizable way of managing such trade-offs. The approach is illustrated and evaluated using a woodland mapping of England as a case study in which reference data is collected under constraints motivated by COVID-19 travel restrictions. The key findings of this paper are as follows: (a) an adaptive approach to sampling reference data allows an informed approach when quantifying this trade-off; and (b) Bayesian inference is naturally suited to adaptive sampling and can make use of Monte Carlo methods when dealing with more advanced problems and analytical techniques.

This paper proposes a Bayesian multilevel spatio-temporal model with a time-varying spatial autoregressive coefficient to estimate temporally heterogeneous network interdependence. To tackle the classic reflection problem, we use multiple factors to control for confounding caused by latent homophily and common exposures. We develop a Markov Chain Monte Carlo algorithm to estimate parameters and adopt Bayesian shrinkage to determine the number of factors. Tests on simulated and empirical data show that the proposed model improves identification of network interdependence and is robust to misspecification. Our method is applicable to various types of networks and provides a simpler and more flexible alternative to coevolution models.

What is the impact of uncommon but notable violent acts on conflict dynamics? We analyze the impact of the murder of a Palestinian child on the broader dynamics of Israeli-Palestinian violence in Jerusalem. By using novel micro-level event data and utilizing Discrete Fourier Transform and Bayesian Poisson Change Point Analysis, we compare the impact of the murder to that of other lethal but more typical Israeli-Palestinian events. We demonstrate that the murder had a large and durable impact on the average number of daily riots in Jerusalem, whereas the other events caused smaller, short-term effects. We demonstrate that scholars should devote more attention to the analysis of atypical violent acts and indicate a set of tools for conducting such analyses.

The knowledge of genetic parameters of performance traits is crucial for any breeding programme in dairy animals. The present study was conducted to use a Bayesian approach for estimation of genetic parameters of production and reproduction traits in Jersey crossbred cattle. Data of Jersey crossbred cattle maintained at Eastern Regional Station, National Dairy Research Institute, West Bengal spread over a span of 41 years were utilized. The marginal posterior medians of heritability for 305-day milk yield (305MY), total milk yield (TMY), peak yield (PY), lactation length (LL), calving interval (CI), total milk yield per day of lactation length (TMY/LL) and total milk yield per day of calving interval (TMY/CI) were 0.31 ± 0.07, 0.29 ± 0.07, 0.27 ± 0.06, 0.16 ± 0.05, 0.15 ± 0.05, 0.29 ± 0.06, 0.27 ± 0.06, respectively. Moderate heritability estimates for 305MY, TMY, PY and production efficiency traits indicate the presence of adequate additive genetic variance in these traits to respond to selection combined with better herd management. Repeatability estimates for 305MY, TMY, PY, LL, CI, TMY/LL and TMY/CI were 0.57 ± 0.08, 0.58 ± 0.08, 0.51 ± 0.07, 0.34 ± 0.06, 0.31 ± 0.06, 0.54 ± 0.07 and 0.49 ± 0.07, respectively. Repeatability estimates for 305MY, TMY and PY were high in the current study, suggesting the use of first lactation records for early evaluation of Jersey crossbred cattle for future selection. Genetic correlations varied from 0.21 to 0.97 and maximum genetic correlation was observed between 305MY and TMY indicating that consideration of 305MY instead of TMY in breeding programmes would suffice. Positive genetic correlations of CI with 305MY and TMY indicated the antagonistic association between production and reproduction traits.

For interventions that affect how individuals interact, social network data may aid in understanding the mechanisms through which an intervention is effective. Social networks may even be an intermediate outcome observed prior to end of the study. In fact, social networks may also mediate the effects of the intervention on the outcome of interest, and Sweet (2019) introduced a statistical model for social networks as mediators in network-level interventions. We build on their approach and introduce a new model in which the network is a mediator using a latent space approach. We investigate our model through a simulation study and a real-world analysis of teacher advice-seeking networks.