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Science Progress 2021A common technique for eliciting subjective probabilities is to provide a set of exclusive and exhaustive events and ask the assessor to estimate the probabilities of...
A common technique for eliciting subjective probabilities is to provide a set of exclusive and exhaustive events and ask the assessor to estimate the probabilities of such events. However, such subjective probabilities estimations are usually subjected to a bias known as the partition dependence bias. This study aims to investigate the effect of state space partitioning and the level of knowledge on subjective probability estimations. The state space is partitioned into full, collapsed, and pruned trees, while the knowledge is manipulated into low and high levels. A scenario called "Best Bank Award" was developed and a 2 × 3 experimental design was employed to explore the effect of the level of knowledge and the partitioning of the state space on the subjective probability. A total of 627 professionals participated in the study and 543 valid responses were used for analysis. The results of two-way ANOVA with the Tukey HSD test for post hoc analysis indicate a mean probability of 24.2% for the full tree, which is significantly lower than those of the collapsed (35.7%) as well as pruned (36.3%) trees. Moreover, there is significant difference in the mean probabilities between the low (38.1%) and high (24.9%) knowledge levels. The results support the hypotheses that the partitioning of the state space as well as the level of knowledge affects subjective probability estimation. The study demonstrates that regardless of the level of knowledge, the partition dependence bias is robust. However, the subjective probability accuracy improves with more knowledge.
Topics: Analysis of Variance; Bias; Probability
PubMed: 33861653
DOI: 10.1177/00368504211009675 -
Clinical Genetics Oct 1989The aim of this paper is to clarify some of the concepts used in the calculation of risk in genetic counselling. The use of probability as a measure of risk in a...
The aim of this paper is to clarify some of the concepts used in the calculation of risk in genetic counselling. The use of probability as a measure of risk in a particular case is reviewed, with special reference to the Fisher-Neyman controversy. The technical concept of likelihood is then introduced and applied to risk probabilities themselves. The difficulties of interpretation are discussed, with a distinction drawn between calculating the risk for a child and inferring the genotype of his mother, even though the risk is wholly determined by the genotype. Fiducial inference is briefly mentioned, and the paper ends with a discussion of the problem which arises when the functional relationship between an unknown parameter and the unknown risk is not 1:1. The conclusion is tentatively reached that risk can only be gauged by probability and thus that prior probabilities must sometimes be assumed, but the paper is primarily intended as a guide and catalyst for the informed discussion of some of the difficulties, which may involve ethical dilemmas.
Topics: Genetic Counseling; Humans; Likelihood Functions; Probability
PubMed: 2805378
DOI: 10.1111/j.1399-0004.1989.tb03192.x -
PloS One 2024Probabilistic models enhance breeding, especially for the Tahiti acid lime, a fruit essential to fresh markets and industry. These models identify superior and...
Probabilistic models enhance breeding, especially for the Tahiti acid lime, a fruit essential to fresh markets and industry. These models identify superior and persistent individuals using probability theory, providing a measure of uncertainty that can aid the recommendation. The objective of our study was to evaluate the use of a Bayesian probabilistic model for the recommendation of superior and persistent genotypes of Tahiti acid lime evaluated in 12 harvests. Leveraging the Monte Carlo Hamiltonian sampling algorithm, we calculated the probability of superior performance (superior genotypic value), and the probability of superior stability (reduced variance of the genotype-by-harvests interaction) of each genotype. The probability of superior stability was compared to a measure of persistence estimated from genotypic values predicted using a frequentist model. Our results demonstrated the applicability and advantages of the Bayesian probabilistic model, yielding similar parameters to those of the frequentist model, while providing further information about the probabilities associated with genotype performance and stability. Genotypes G15, G4, G18, and G11 emerged as the most superior in performance, whereas G24, G7, G13, and G3 were identified as the most stable. This study highlights the usefulness of Bayesian probabilistic models in the fruit trees cultivars recommendation.
Topics: Humans; Bayes Theorem; Plant Breeding; Probability; Polynesia; Oxides; Calcium Compounds
PubMed: 38442106
DOI: 10.1371/journal.pone.0299290 -
Journal of Personality and Social... Aug 2016Forecasted probabilities rarely stay the same for long. Instead, they are subject to constant revision-moving upward or downward, uncertain events become more or less...
Forecasted probabilities rarely stay the same for long. Instead, they are subject to constant revision-moving upward or downward, uncertain events become more or less likely. Yet little is known about how people interpret probability estimates beyond static snapshots, like a 30% chance of rain. Here, we consider the cognitive, affective, and behavioral consequences of revisions to probability forecasts. Stemming from a lay belief that revisions signal the emergence of a trend, we find in 10 studies (comprising uncertain events such as weather, climate change, sex, sports, and wine) that upward changes to event-probability (e.g., increasing from 20% to 30%) cause events to feel less remote than downward changes (e.g., decreasing from 40% to 30%), and subsequently change people's behavior regarding those events despite the revised event-probabilities being the same. Our research sheds light on how revising the probabilities for future events changes how people manage those uncertain events. (PsycINFO Database Record
Topics: Adult; Humans; Probability; Thinking; Uncertainty; Young Adult
PubMed: 27281350
DOI: 10.1037/pspa0000058 -
Annual International Conference of the... Aug 2016Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of...
Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of sensor technology, the number of studies dedicated to reliable fall detection system has increased drastically. However, there is still a lack of universal approach regarding the evaluation of developed algorithms. In the following study we make an attempt to find publicly available fall datasets and analyze similarities among them using supervised learning. After preforming similarity assessment based on multidimensional scaling we indicate the most representative feature vector corresponding to each specific dataset. This vector obtained from a real-life data is subsequently deployed to estimate fall risk probabilities for a statistical fall detection model. Finally, we conclude with some observations regarding the similarity assessment results and provide suggestions towards an efficient approach for evaluation of fall detection studies.
Topics: Accidental Falls; Algorithms; Humans; Models, Statistical; Probability; Risk Assessment; Supervised Machine Learning
PubMed: 28268437
DOI: 10.1109/EMBC.2016.7590811 -
Methods of Information in Medicine 2012Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient...
BACKGROUND
Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.
OBJECTIVES
The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.
METHODS
Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians.
RESULTS
Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software.
CONCLUSIONS
Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.
Topics: Artificial Intelligence; Computer Simulation; Humans; Learning; Logistic Models; Models, Statistical; Probability; Statistics as Topic; Statistics, Nonparametric
PubMed: 21915433
DOI: 10.3414/ME00-01-0052 -
Science (New York, N.Y.) Sep 2011We are all faced with uncertainty about the future, but we can get the measure of some uncertainties in terms of probabilities. Probabilities are notoriously difficult... (Review)
Review
We are all faced with uncertainty about the future, but we can get the measure of some uncertainties in terms of probabilities. Probabilities are notoriously difficult to communicate effectively to lay audiences, and in this review we examine current practice for communicating uncertainties visually, using examples drawn from sport, weather, climate, health, economics, and politics. Despite the burgeoning interest in infographics, there is limited experimental evidence on how different types of visualizations are processed and understood, although the effectiveness of some graphics clearly depends on the relative numeracy of an audience. Fortunately, it is increasingly easy to present data in the form of interactive visualizations and in multiple types of representation that can be adjusted to user needs and capabilities. Nonetheless, communicating deeper uncertainties resulting from incomplete or disputed knowledge--or from essential indeterminacy about the future--remains a challenge.
Topics: Computer Graphics; Data Display; Forecasting; Humans; Information Dissemination; Probability; Risk; Uncertainty
PubMed: 21903802
DOI: 10.1126/science.1191181 -
Physical Review. E Feb 2019Evolution on changing fitness landscapes (seascapes) is an important problem in evolutionary biology. We consider the Moran model of finite population evolution with...
Evolution on changing fitness landscapes (seascapes) is an important problem in evolutionary biology. We consider the Moran model of finite population evolution with selection in a randomly changing, dynamic environment. In the model, each individual has one of the two alleles, wild type or mutant. We calculate the fixation probability by making a proper ansatz for the logarithm of fixation probabilities. This method has been used previously to solve the analogous problem for the Wright-Fisher model. The fixation probability is related to the solution of a third-order algebraic equation (for the logarithm of fixation probability). We consider the strong interference of landscape fluctuations, sampling, and selection when the fixation process cannot be described by the mean fitness. Such an effect appears if the mutant allele has a higher fitness in one landscape and a lower fitness in another, compared with the wild type, and the product of effective population size and fitness is large. We provide a generalization of the Kimura formula for the fixation probability that applies to these cases. When the mutant allele has a fitness (dis-)advantage in both landscapes, the fixation probability is described by the mean fitness.
Topics: Alleles; Markov Chains; Models, Genetic; Probability; Selection, Genetic
PubMed: 30934266
DOI: 10.1103/PhysRevE.99.022407 -
Osteoporosis International : a Journal... Jan 2022We compared, for women in Pakistan, the utility of intervention thresholds either at a T-score ≤ - 2.5 or based on a FRAX probability equivalent to women of...
UNLABELLED
We compared, for women in Pakistan, the utility of intervention thresholds either at a T-score ≤ - 2.5 or based on a FRAX probability equivalent to women of average body mass index (BMI) with a prior fragility fracture. Whereas the FRAX-based intervention threshold identified women at high fracture probability, the T-score threshold was less sensitive, and the associated fracture risk decreased markedly with age.
PURPOSE
The fracture risk assessment algorithm FRAX® has been recently calibrated for Pakistan, but guidance is needed on how to apply fracture probabilities to clinical practice.
METHODS
The age-specific 10-year probabilities of a major osteoporotic fracture were calculated in women with average BMI to determine fracture probabilities at two potential intervention thresholds. The first comprised the age-specific fracture probabilities associated with a femoral neck T-score of - 2.5. The second approach determined age-specific fracture probabilities that were equivalent to a woman with a prior fragility fracture, without bone mineral density (BMD). The parsimonious use of BMD was additionally explored by the computation of upper and lower assessment thresholds for BMD testing.
RESULTS
When a BMD T-score ≤ - 2.5 was used as an intervention threshold, FRAX probabilities in women aged 50 years were approximately two-fold higher than in women of the same age but with no risk factors and average BMD. The relative increase in risk associated with the BMD threshold decreased progressively with age such that, at the age of 80 years or more, a T-score of - 2.5 was actually protective. The 10-year probability of a major osteoporotic fracture by age, equivalent to women with a previous fracture, rose with age from 2.1% at the age of 40 years to 17%, at the age of 90 years, and identified women at increased risk at all ages.
CONCLUSION
Intervention thresholds based on BMD alone do not effectively target women at high fracture risk, particularly in the elderly. In contrast, intervention thresholds based on fracture probabilities equivalent to a 'fracture threshold' target women at high fracture risk.
Topics: Adult; Aged; Aged, 80 and over; Bone Density; Female; Humans; Osteoporotic Fractures; Pakistan; Risk Assessment; Risk Factors
PubMed: 34414463
DOI: 10.1007/s00198-021-06087-y -
Perspectives on Psychological Science :... Nov 2015Intelligence analysis plays a vital role in policy decision making. Key functions of intelligence analysis include accurately forecasting significant events,... (Review)
Review
Intelligence analysis plays a vital role in policy decision making. Key functions of intelligence analysis include accurately forecasting significant events, appropriately characterizing the uncertainties inherent in such forecasts, and effectively communicating those probabilistic forecasts to stakeholders. We review decision research on probabilistic forecasting and uncertainty communication, drawing attention to findings that could be used to reform intelligence processes and contribute to more effective intelligence oversight. We recommend that the intelligence community (IC) regularly and quantitatively monitor its forecasting accuracy to better understand how well it is achieving its functions. We also recommend that the IC use decision science to improve these functions (namely, forecasting and communication of intelligence estimates made under conditions of uncertainty). In the case of forecasting, decision research offers suggestions for improvement that involve interventions on data (e.g., transforming forecasts to debias them) and behavior (e.g., via selection, training, and effective team structuring). In the case of uncertainty communication, the literature suggests that current intelligence procedures, which emphasize the use of verbal probabilities, are ineffective. The IC should, therefore, leverage research that points to ways in which verbal probability use may be improved as well as exploring the use of numerical probabilities wherever feasible.
Topics: Decision Making; Forecasting; Humans; Probability; Public Policy; Uncertainty; United States; United States Department of Defense
PubMed: 26581731
DOI: 10.1177/1745691615598511