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-1-1Scientific Resources
https://www.merlot.org/merlot/viewMaterial.htm?id=79527
Large collection of scientific resources about statistics (descriptive statistics, testing, and continuous distributions), econometrics, and time series analysis.Tue, 10 Jun 2003 07:00:00 GMTBorghers and WessaBayesian Revised Probability
https://www.merlot.org/merlot/viewMaterial.htm?id=76971
It is JavaScript that computes Bayes' posterior discrete probabilities given a subjective prior probability vector and the reliability matrix obtained from an expert judgment. Applications to decision analysis with maximum number of nine states of nature are also included.Thu, 30 May 2002 07:00:00 GMTBarbra Bied Sperling CSU, Office of the ChancellorRevising the Mean and the Variance
https://www.merlot.org/merlot/viewMaterial.htm?id=78131
It is a JavaScript that combines the subjective estimates with the evidence-based estimates for the mean and the variance.Fri, 15 Nov 2002 08:00:00 GMTBarbra Bied Sperling CSU, Office of the ChancellorBayesian Inference for the Mean
https://www.merlot.org/merlot/viewMaterial.htm?id=82160
It is a JavaScript that performs the Bayesian inference by combining the sample information with a prior information.Fri, 01 Oct 2004 07:00:00 GMTBarbra Bied Sperling CSU, Office of the ChancellorSubjectivity in Hypothesis Testing
https://www.merlot.org/merlot/viewMaterial.htm?id=78127
A JavaScript that computes the total net cost/profit in statistical testing given the prior probabilities with respect to the trustfulness of the null hypothesis.Fri, 15 Nov 2002 08:00:00 GMTBarbra Bied Sperling CSU, Office of the ChancellorThink Bayes: Bayesian Statistics Made Simple
https://www.merlot.org/merlot/viewMaterial.htm?id=1370247
<p><em>Think Bayes</em> is an introduction to Bayesian statistics using computational methods.</p>
<p>The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.</p>
<p>Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.</p>
<p>I think this presentation is easier to understand, at least for people with programming skills. It is also more general because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems.</p>Tue, 27 Feb 2018 23:03:11 GMTAllen B. Downey Franklin W. Olin College of Engineering