25 papers · ranked by Valyu relevance
Eric-Jan Wagenmakers, Maarten Marsman, Tahira Jamil, Alexander Ly + 9 more
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…
Luai Al-Labadi, Zeynep Baskurt, Michael Evans
The features of a logically sound approach to a theory of statistical reasoning are discussed. A particular approach that satisfies these criteria is reviewed. This is seen to involve selection of a model, model checking, elicitation of a prior, checking the prior for bias, checking for prior-data conflict and…
Aris Spanos, Brian Dennis, Mark L. Taper, Jose Miguel Ponciano + 1 more
'Geert Verdoolaege'] The paper makes a case that the current discussions on replicability and the abuse of significance testing have overlooked a more general contributor to the untrustworthiness of published empirical evidence, which is the uninformed and recipe-like implementation of statistical modeling and…
Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski + 1 more
'Jukka Corander'] Title: Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference…
Luai Al‐Labadi, Zeynep Baskurt, Michael Evans
The features of a logically sound approach to a theory of statistical reasoning are discussed. A particular approach that satisfies these criteria is reviewed. This is seen to involve selection of a model, model checking, elicitation of a prior, checking the prior for bias, checking for prior-data conflict and…
Keli Liu, Xiao‐Li Meng
Doctors use statistics to advance medical knowledge; we use a medical analogy to introduce statistical inference "from scratch" and to highlight an improvement. Your doctor, perhaps implicitly, predicts the effectiveness of a treatment for you based on its performance in a clinical trial; the trial patients serve as…
Roberto Trotta
These notes aim at presenting an overview of Bayesian statistics, the underlying concepts and application methodology that will be useful to astronomers seeking to analyse and interpret a wide variety of data about the Universe. The level starts from elementary notions, without assuming any previous knowledge of…
Russell J. Bowater
An overview is presented of a general theory of statistical inference that is referred to as the fiducial-Bayes fusion. This theory combines organic fiducial inference and Bayesian inference. The aim is that the reader is given a clear summary of the conceptual framework of the fiducial-Bayes fusion as well as pointers…
Roberto Rossi, Özgür Akgün, Steven Prestwich, Ş. Armağan Tarim
In this work we introduce declarative statistics, a suite of declarative modelling tools for statistical analysis. Statistical constraints represent the key building block of declarative statistics. First, we introduce a range of relevant counting and matrix constraints and associated decompositions, some of which…
Michael Betancourt
As the frontiers of applied statistics progress through increasingly complex experiments we must exploit increasingly sophisticated inferential models to analyze the observations we make. In order to avoid misleading or outright erroneous inferences we then have to be increasingly diligent in scrutinizing the…
Christopher Harms, Daniël Lakens
Being able to interpret ‘null effects?is important for cumulative knowledge generation in science. To draw informative conclusions from null-effects, researchers need to move beyond the incorrect interpretation of a non-significant result in a null-hypothesis significance test as evidence of the absence of an effect.…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 4 more
In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. It can reproduce causal induction of human judgment…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
Sebahat Gok, Robert L. Goldstone
Interactive computer simulations are commonly used as pedagogical tools to support students’ statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks-dual processes and grounded cognition-in the context of people’s…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Paul Vos
This paper examines the foundational concept of random variables in probability theory and statistical inference, demonstrating that their mathematical definition requires no reference to randomization or hypothetical repeated sampling. We show how measure-theoretic probability provides a framework for modeling…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 6 more
'Tatsuji Takahashi' 'Hiroshi Okamoto' 'Yukio Pegio Gunji' 'Yoshihiro Nakajima' 'Shunji Mitsuyoshi' 'Enrico Scalas'] Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is…
Danilo Bzdok, Denis Engemann, Olivier Grisel, Gaël Varoquaux + 1 more
In the 20^th^ century many advances in biological knowledge and evidence-based medicine were supported by p-values and accompanying methods. In the beginning 21^st^ century, ambitions towards precision medicine put a premium on detailed predictions for single individuals. The shift causes tension between traditional…
Rand R. Wilcox, Guillaume A. Rousselet
There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of a Type I error, and yielding a deeper and more nuanced understanding of neuroscience data. These new techniques…
Caroline Keck, Axel Mayer, Yves Rosseel
Within the framework of constrained statistical inference, we can test informative hypotheses, in which, for example, regression coefficients are constrained to have a certain direction or be in a specific order. A large amount of frequentist informative test statistics exist that each come with different versions…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Emilio Salinas, Terrence R Stanford
Intuitively, combining multiple sources of evidence should lead to more accurate decisions than considering single sources of evidence individually. In practice, however, the proper computation may be difficult, or may require additional data that are inaccessible. Here, based on the concept of conditional…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…