Article

Philosophy and the practice of Bayesian statistics.

Department of Statistics and Department of Political Science, Columbia University, New York, USA Statistics Department, Carnegie Mellon University, Santa Fe Institute, Pittsburgh, USA.
British Journal of Mathematical and Statistical Psychology (Impact Factor: 1.53). 02/2012; DOI: 10.1111/j.2044-8317.2011.02037.x
Source: PubMed

ABSTRACT A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework.

0 Bookmarks
 · 
135 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We investigate the influence of perceived displacement of moving agent-like stimuli on the performance in dynamic interactive tasks. In order to reliably measure perceived displacement we utilize multiple tasks with different task demands. The perceived center of an agent's body is displaced in the direction in which the agent is facing and this perceived displacement is larger than the theoretical position of the center of mass would predict. Furthermore, the displacement in the explicit judgment is dissociated from the displacement obtained by the implicit measures. By manipulating the location of the pivot point, we show that it is not necessary to postulate orientation as an additional cue utilized by perception, as has been suggested by earlier studies. These studies showed that the agent's orientation influences the detection of chasing motion and the detection-related performance in interactive tasks. This influence has been labeled wolfpack effect. In one of the demonstrations of the wolfpack effect participants control a green circle on a display with a computer mouse. It has been shown that participants avoid display areas with agents pointing toward the green circle. Participants do so in favor of areas where the agents point in the direction perpendicular to the circle. We show that this avoidance behavior arises because the agent's pivot point selected by the earlier studies is different from where people locate the center of agent's body. As a consequence, the nominal rotation confounds rotation and translation. We show that the avoidance behavior disappears once the pivot point is set to the center of agent's body.
    Frontiers in Psychology 01/2014; 5:1423. · 2.80 Impact Factor
  • Source
    Dataset: falsifiable
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: http://authors.elsevier.com/a/1QPar4sKhE0yPC Accurate forecasts of water demand are required for real-time control of water supply systems under normal and abnormal conditions. A methodology is presented for quantifying, diagnosing and reducing model structural and predictive errors for the development of short term water demand forecasting models. The methodology (re-)emphasises the importance of posterior predictive checks of modelling assumptions in model development, and to account for inherent demand uncertainty, quantifies model performance probabilistically through evaluation of the sharpness and reliability of model predictive distributions. The methodology, when applied to forecast demand for three District Meter Areas in the UK, revealed the inappropriateness of simplistic Gaussian residual assumptions in demand forecasting. An iteratively revised, parsimonious model using a formal Bayesian likelihood function that accounts for kurtosis and heteroscedasticity in the residuals led to sharper yet reliable predictive distributions that better quantifies the time varying nature of demand uncertainty across the day in water supply systems.
    Environmental Modelling and Software 04/2015; 66:87-97. · 4.54 Impact Factor

Preview

Download
3 Downloads
Available from