28,542 research outputs found
Cosmological parameter inference with Bayesian statistics
Bayesian statistics and Markov Chain Monte Carlo (MCMC) algorithms have found
their place in the field of Cosmology. They have become important mathematical
and numerical tools, especially in parameter estimation and model comparison.
In this paper, we review some fundamental concepts to understand Bayesian
statistics and then introduce MCMC algorithms and samplers that allow us to
perform the parameter inference procedure. We also introduce a general
description of the standard cosmological model, known as the CDM
model, along with several alternatives, and current datasets coming from
astrophysical and cosmological observations. Finally, with the tools acquired,
we use an MCMC algorithm implemented in python to test several cosmological
models and find out the combination of parameters that best describes the
Universe.Comment: 30 pages, 17 figures, 5 tables; accepted for publication in Universe;
references adde
Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers
ABSTRACTIn this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey. The results suggest that Bayesian analysis of data sets collected from moral educational studies can provide additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program, and thresholding based on Bayes Factors, to scholars in the field of moral education
Philosophy and the practice of Bayesian statistics
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.Comment: 36 pages, 5 figures. v2: Fixed typo in caption of figure 1. v3:
Further typo fixes. v4: Revised in response to referee
Robust Bayesian inference in elliptical regression models
Bayesian Statistics
Bayesian efficiency analysis with a flexible form: The aim cost function
Sampling;Bayesian Statistics
Reference priors in non-normal location problems
Bayesian Statistics;Statistical Distribution
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