Principled Decision-Making Workflow with Hierarchical Bayesian Models of High-Throughput Dose-Response Measurements

We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.

Principled decision-making workflow with hierarchical Bayesian models of high throughput dose-response measurements

We present a case study applying hierarchical Bayesian estimation on high throughput protein melting point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting temperature posterior distribution estimates to enable principled decision-making in common high throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve fitting. We conclude with a discussion of the relative merits of each workflow.

A triangulation estimation and forecasting framework for agricultural time series

This study proposes a framework implementing triangular estimation for better modeling and forecasting time series. In order to improve the performance of estimation, we employ two sources of triangulation to generate a time series, which is statistically indistinguishable with the latent time series hidden in a system. Thanks to Bayesian hierarchical estimation, which is akin to deep learning but more sophisticate and longer history, the framework has been validated by a large amount of records in vegetable auctions. The hierarchical Bayesian estimation and Monte Carlo Markov Chain particle filters used in hidden Markov model are appreciated during the massive bootstrapping of data. Our results demonstrate excellent estimation performance in discovering hidden states.

Choosing for another: Social context changes dissociable computational mechanisms of risky decision-making

Choices under conditions of risk often have consequences not just for ourselves, but for others. Yet, it is unclear how the other’s identity (stranger, close friend, etc.) influences risky choices made on their behalf. Here, two groups of undergraduates made a series of risky economic decisions for themselves, for another person, or for both themselves and another person (i.e., shared outcomes); one group of participants made choices involving a same-sex stranger (n = 29), the other made choices involving a same-sex close friend (n = 28). Hierarchical Bayesian Estimation of computations underlying risky decision-making revealed that relative to choosing for themselves, people were more risk averse, more loss averse, and more consistent when choices involved another person. Interestingly, partner identity differentially modulated decision computations. People became risk neutral and more consistent when choosing for friends relative to strangers. In sum, these findings suggest that the complexity of the social world is mirrored in its nuanced consequences for our choices.

Commentary: Altered learning under uncertainty in unmedicated mood and anxiety disorders

Understanding how anxious and depressed individuals process information is a central topic in the field of psychiatry. In this regard, Aylward et al. (2019) utilized computational models of learning to better understand and describe how anxious and/or depressed individuals behave from moment to moment when faced with uncertain situations. Participants performed decision-making tasks characterized by fluctuating rewards and punishment. The authors fit computational models to the collected data from participants in the anxiety and healthy groups using the hierarchical Bayesian estimation with two levels of priors, at individual and group levels, where they set the group prior separately for each group. The three parameters of the winning model (punishment learning rate, lapse parameter, and decay rate) were higher in the symptomatic group than in the healthy group. In short, the authors found that anxious individuals quickly learned about negative phenomena but not about positive phenomena. Notwithstanding, we believe we have identified two methodological issues regarding the statistical analysis of the cited study, shrinkage and a ‘two-step approach’.