We’ll add more features and details as we proceed.Ī Jupyter notebook is displayed on a web browser on a computer, tablet (e.g., IPad), or even your smartphone. This is a whirlwind tour of just the minimum we need to know about Python and Jupyter notebooks to get started doing statistics problems. The “User Interface Tour” and “Keyboard Shortcuts” are useful places to start, but there are also many other links to documentation there. Last revised: 1 by Dick Furnstahl can find valuable documentation under the Jupyter notebook Help menu. Standard medical example by applying Bayesian rules of probability Mini-project IIIb: Bayesian Neural NetworksĬhecking the sum and product rules, and their consequences Mini-project IIb: How many lines are there? Mini-project I: Parameter estimation for a toy model of an EFT Linear algebra games including SVD for PCA What is a convolutional neural network?ġ0.2. Variational Inference: Bayesian Neural Networksĩ.10. Neural network classifier demonstrationĩ.9.
Viewing method parameter python jupyter notebook pdf#
Making figures for Ignorance PDF notebookĩ.6. Maximum Entropy for reconstructing a function from its momentsĨ.5. Ma圎nt for deriving some probability distributionsĨ.4. Ignorance pdfs: Indifference and translation groupsĨ.3. Exercise: Gaussian Process models with GPyĨ.2. Learning from data: Gaussian processesħ.4. zeus: Sampling from multimodal distributionsħ.3.
Comparing samplers for a simple problemĦ.9. Solving orbital equations with different algorithmsĦ.8. Quick check of the distribution of normal variables squaredĦ.4. Example: Parallel tempering for multimodal distributions vs. Example: Parallel tempering for multimodal distributionsĥ.6. Evidence calculation for EFT expansionsĥ.5. Building intuition about correlations (and a bit of Python linear algebra)ĥ.3. Visualization of the Central Limit TheoremĤ.8. Error propagation: Example 3.6.2 in SiviaĤ.7. Parameter estimation example: fitting a straight line IIĤ.6. Metropolis-Hasting MCMC sampling of a Poisson distributionĤ.4. Follow-up: fluctuation trends with # of points and data errorsģ.2. Linear algebra games including SVD for PCAĢ.12. Assignment: Follow-ups to Parameter Estimation notebooksĢ.11. Amplitude of a signal in the presence of backgroundĢ.9. Linear Regression and Model Validation demonstrationĢ.8. Parameter estimation example: fitting a straight lineĢ.6. Assignment: 2D radioactive lighthouse location using MCMCĢ.5. Parameter estimation example: Gaussian noise and averagesĢ.3. Standard medical example by applying Bayesian rules of probabilityĢ.2. Interactive Bayesian updating: coin flipping exampleġ.6. Checking the sum and product rules, and their consequencesġ.5.
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