This is a new and experimental feature of this course. All demonstrations use the Wolfram Cloud server. You don’t need an account at Wolfram Research, although this may change in the future.
The server response can be slow. For navigation you could use the “<” or “>” buttons of the web browser. However, it is faster to return to this page and relaunch the demonstration again, which is always opened in a fresh webpage.
A random polynomial function: randomPolynomialFunctionPlot. Just a test of these demo’s.
Earnings example. Bayesian Prior probability: priorPolynomialModelPlot.
Earnings example. Bayesian Posterior probability: posteriorPolynomialModelPlot.
Earnings example. Maximum Likelihood solution: polynomialMaximumLikelihoodSolutionPlot.
Earnings example. Bayesian Maximum Posterior (MAP) solution: polynomialMaximumPosteriorSolutionPlot.
Bishop’s example. Random solutions near the Bayesian MAP solution: polynomialMAPRandomSolutionPlot.
Bishop’s example. Bayesian Model selection from 10 polynomials: polynomialModelSelectionPlot.
Bishop’s example. Bayesian Prediction of the future: polynomialPredictiveDistributionPlot.
Bishop’s example. The effect of missing data: polynomialMissingDataPlot.
Neural network example. The layout of a neural network: neuralNetworkLayoutPlot.
Neural network example. Bayesian neural network solution: posteriorNeuralNetworkPlot.