Bayesian model
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- MeSH
- lidé MeSH
- matematika MeSH
- pravděpodobnost MeSH
- teoretické modely MeSH
- výuka - hodnocení MeSH
- Check Tag
- lidé MeSH
Zpracování znalostí zatížených nejistotou je jednou z nejdůležitějších aplikací metod umělé inteligence. Použití technologie bayesovských sítí umožňuje pro tyto ucely využít výsledky po několik století budované teorie pravděpodobnosti a pracovat s mnohorozměrnými pravdepodobnostními distribucemi V tomto případě muže být rozměr distribucí roven stovkám, případně i tisícům. To znamená, že tato technologie může být použita na reálné aplikace, na skutečné problémy, jejichž složitost přesahuje možnosti většiny dalších přístupů pro modelování nejistých znalostí. Vzhledem k tomu, že se jedná o poměrně mladou disciplínu, nelze říci, že všechny teoretické problémy a problémy spojené s návrhem aplikací již byly úspěšně vyřešeny. Nejvíce otevřených problémů je spojeno právě s konstrukcí bayesovských sítu Přesto sejižobjevují aplikace, které naznačují, že bayesovské sítě se stanoujednítn z mocných nástrojů umělé inteligence pro řešení složitých problémů. Proto lze předpokládat, že se s bayesovskými sítěmi budeme v blízké budoucnosti setkávat i v medicíně, která je jednou z oblastí, kde deterministická znalost je spíše výjimkou.
Uncertain knowledge processing is one of the most important applications of artificial intelligence. Bayesian network technology, taking advantage of for several centuries developed results of probability theory, enables processing of multidimensional probability distributions whose dimensionality equals hundreds or even thousands. Therefore, this technology can be applied to real-life problems whose complexity goes beyond cambility of most other approaches for uncertain knowledge processing. It cannot be said that this relatively new discipline has Iready solved all its theoretical and practical problems. Most of still open problems are connected with zonstraction of Bayesian network models for practical applications. Nevertheless, recently published applications suggest that Bayesian network will become one of he most powerful tool of artificial intelligence for uncertain knowledge processing. Therefore, we can assume that in near future we shall meet Bayesian network in medical applications as this field is one of those where deterministic knowledge is exception.
One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two populations are assumed to have the same variance. As an alternative to both methods, we outline a comprehensive t test framework based on Bayesian model averaging. This new t test framework simultaneously takes into account models that assume equal and unequal variances, and models that use t-likelihoods to improve robustness to outliers. The resulting inference is based on a weighted average across the entire model ensemble, with higher weights assigned to models that predicted the observed data well. This new t test framework provides an integrated approach to assumption checks and inference by applying a series of pertinent models to the data simultaneously rather than sequentially. The integrated Bayesian model-averaged t tests achieve robustness without having to commit to a single model following a series of assumption checks. To facilitate practical applications, we provide user-friendly implementations in JASP and via the RoBTT package in R . A tutorial video is available at https://www.youtube.com/watch?v=EcuzGTIcorQ.
- MeSH
- Bayesova věta MeSH
- experimentální psychologie * metody MeSH
- interpretace statistických dat MeSH
- lidé MeSH
- statistické modely * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Human and animal diet reconstruction studies that rely on tissue chemical signatures aim at providing estimates on the relative intake of potential food groups. However, several sources of uncertainty need to be considered when handling data. Bayesian mixing models provide a natural platform to handle diverse sources of uncertainty while allowing the user to contribute with prior expert information. The Bayesian mixing model FRUITS (Food Reconstruction Using Isotopic Transferred Signals) was developed for use in diet reconstruction studies. FRUITS incorporates the capability to account for dietary routing, that is, the contribution of different food fractions (e.g. macronutrients) towards a dietary proxy signal measured in the consumer. FRUITS also provides relatively straightforward means for the introduction of prior information on the relative dietary contributions of food groups or food fractions. This type of prior may originate, for instance, from physiological or metabolic studies. FRUITS performance was tested using simulated data and data from a published controlled animal feeding experiment. The feeding experiment data was selected to exemplify the application of the novel capabilities incorporated into FRUITS but also to illustrate some of the aspects that need to be considered when handling data within diet reconstruction studies. FRUITS accurately predicted dietary intakes, and more precise estimates were obtained for dietary scenarios in which expert prior information was included. FRUITS represents a useful tool to achieve accurate and precise food intake estimates in diet reconstruction studies within different scientific fields (e.g. ecology, forensics, archaeology, and dietary physiology).
- MeSH
- analýza potravin metody statistika a číselné údaje MeSH
- Bayesova věta MeSH
- dieta * MeSH
- izotopy dusíku MeSH
- krmivo pro zvířata analýza MeSH
- lidé MeSH
- nejistota MeSH
- statistické modely * MeSH
- stravovací zvyklosti fyziologie psychologie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
3rd ed. xv, 351 s. : il.
BACKGROUND: We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process. METHODS: We illustrate the application of the outlined Bayesian approaches on an example data set, retrospective re-analyzing a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study. RESULTS: In the retrospective re-analysis of the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package. CONCLUSIONS: The outlined Bayesian framework provides several benefits when applied to parametric survival analyses. It uses data more efficiently, is capable of considerably shortening the length of clinical trials, and provides a richer set of inferences.
Statistics in practice
1st ed. xi, 266 s.
Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).
- MeSH
- Bayesova věta * MeSH
- lidé MeSH
- psychologie * MeSH
- výzkumný projekt MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.
- MeSH
- Bayesova věta * MeSH
- exekutivní funkce fyziologie MeSH
- interpretace statistických dat * MeSH
- lidé MeSH
- neuropsychologické testy * MeSH
- posilování (psychologie) * MeSH
- psychologické modely * MeSH
- rozhodování fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.
- MeSH
- Bayesova věta * MeSH
- lidé MeSH
- psychologie * MeSH
- software * MeSH
- výzkumný projekt MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
