Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

He, Jingjing; Wang, Wei; Huang, Min; Wang, Shaohua; Guan, Xuefei

doi:10.3390/math9212810

Cited by 9 publications

(6 citation statements)

References 43 publications

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“…At each assessment point, the hypothesis that the interaction effect (ie, an intervention effect) was different from zero was tested using contrasts comparing each assessment point with the baseline. Moreover, no prior information was included in the analysis, resulting in flat prior distributions [ 66 ]. The probability of the collected data emerging under the null model (ie, meaning that the data were a collection of random noise) was compared with the alternative model (ie, condition [intervention vs CAU] had an effect on outcome).…”

Section: Methodsmentioning

confidence: 99%

Challenges in Recruiting University Students for Web-Based Indicated Prevention of Depression and Anxiety: Results From a Randomized Controlled Trial (ICare Prevent)

Bolinski¹,

Kleiboer²,

Neijenhuijs³

et al. 2022

J Med Internet Res

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Background Depression and anxiety are common mental health conditions in college and university student populations. Offering transdiagnostic, web-based prevention programs such as ICare Prevent to those with subclinical complaints has the potential to reduce some barriers to receiving help (eg, availability of services, privacy considerations, and students’ desire for autonomy). However, uptake of these interventions is often low, and accounts of recruitment challenges are needed to complement available effectiveness research in student populations. Objective The aims of this study were to describe recruitment challenges together with effective recruitment strategies for ICare Prevent and provide basic information on the intervention’s effectiveness. Methods A 3-arm randomized controlled trial was conducted in a student sample with subclinical symptoms of depression and anxiety on the effectiveness of an individually guided (human support and feedback on exercises provided after each session, tailored to each participant) and automatically guided (computer-generated messages provided after each session, geared toward motivation) version of ICare Prevent, a web-based intervention with transdiagnostic components for the indicated prevention of depression and anxiety. The intervention was compared with care as usual. Descriptive statistics were used to outline recruitment challenges and effective web-based and offline strategies as well as students’ use of the intervention. A basic analysis of intervention effects was conducted using a Bayesian linear mixed model, with Bayes factors reported as the effect size. Results Direct recruitment through students’ email addresses via the central student administration was the most effective strategy. Data from 35 participants were analyzed (individually guided: n=14, 40%; automatically guided: n=8, 23%; care as usual: n=13, 37%). Use of the intervention was low, with an average of 3 out of 7 sessions (SD 2.9) completed. The analyses did not suggest any intervention effects other than anecdotal evidence (all Bayes factors10≤2.7). Conclusions This report adds to the existing literature on recruitment challenges specific to the student population. Testing the feasibility of recruitment measures and the greater involvement of the target population in their design, as well as shifting from direct to indirect prevention, can potentially help future studies in the field. In addition, this report demonstrates an alternative basic analytical strategy for underpowered randomized controlled trials. Trial Registration International Clinical Trials Registry Platform NTR6562; https://tinyurl.com/4rbexzrk International Registered Report Identifier (IRRID) RR2-10.1186/s13063-018-2477-y

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Section: Methodsmentioning

confidence: 99%

Challenges in Recruiting University Students for Web-Based Indicated Prevention of Depression and Anxiety: Results From a Randomized Controlled Trial (ICare Prevent)

Bolinski¹,

Kleiboer²,

Neijenhuijs³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Here, the load effect on task performance is expected to be obvious based on data from Experiment 1 and a medium-tolarge effect size would therefore be a conservative estimate. Furthermore, the primary analysis for the choice data (i.e., Bayesian hierarchical modeling) aggregates all information across trials and participants during model fitting, and consequently reducing parameters' sensitivity to noise within each participant and to smaller sample sizes (He, Wang, Huang, Wang, & Guan, 2021;McNeish & Stapleton, 2016;Van de Schoot, Broere, Perryck, & Loey, 2015). All participants reported normal (or corrected-to-normal) vision and color vision and provided informed consent was given before the study began.…”

Section: Participantsmentioning

confidence: 99%

Effortfulness of visual working memory: Gauged by physical exertion.

Zhang¹

2023

Journal of Experimental Psychology: General

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Active maintenance of information in working memory (WM) is an essential but effortful cognitive process. Yet, the effortful nature of WM remains poorly understood. Here, we constructed a model to evaluate how perceived effort of WM is directly compared to that of physical exertion. In Experiment 1, participants freely chose to either remember a certain number of colors in a visual WM task or hold a hand dynamometer to a required percentage of maximal voluntary contraction (%MVC) to obtain a fixed task credit upon successful task completion. We found that participants discounted WM-related effort in the same way as they discounted handgrip-related effort based on a computation of expected choice outcomes (hence utility) associated with different task loads. This rationality in an observer's prospective choice in Experiment 1 was generalized to retrospective choice in Experiment 2 where participants reported which task was more effortful immediately after they had performed both tasks in a randomized order without any reward or feedback. Experiment 3 further probed this shared mechanism using a dual-task paradigm. As predicted by our model, we found that physical exertion could disrupt the performance in the concurrent WM task, proportional to the iso-effort relationship between WM and physical exertion when task loads were high for both tasks. Collectively, our findings converge on a shared computational principle connecting task load, perceived effort, and choice utility across physical and cognitive domains.

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“…Another important parking-related feature is the density of points of interest (POI). We calculate the density of POI using a kernel function [44] presented in (10).…”

Section: Data Declarationmentioning

confidence: 99%

“…However, these smart parking services and applications are still struggling for mass deployment due to the lack of a practical approach to small-sample parking occupancy prediction (POP), i.e., a small-sample prediction problem [10]. Traditional data-hungry models are unable to predict with high accuracy and stability in a data-poor scenario, resulting in insufficient quality of service provided, which seriously undermines users' trust [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Liu

et al. 2022

Mathematics

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Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102x faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.

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Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

Cited by 9 publications

References 43 publications

Challenges in Recruiting University Students for Web-Based Indicated Prevention of Depression and Anxiety: Results From a Randomized Controlled Trial (ICare Prevent)

Challenges in Recruiting University Students for Web-Based Indicated Prevention of Depression and Anxiety: Results From a Randomized Controlled Trial (ICare Prevent)

Effortfulness of visual working memory: Gauged by physical exertion.

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

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