Description
Assignment due Saturday, April 6, 2024 by 11:00pm
Instructions
Complete all the required tasks in the assignment. There are also some optional tasks for extra credit.
Create a new Python notebook with all your code, results, and interpretation of your results. The Forum Workbook (a must) and the Python notebook will be your second deliverable for the assignment.
Keep it professional! Imagine you are writing your code and text for a client. You will be graded accordingly.
Policy for the use of AI tools at ZU.
At the end of this assignment, you should carefully document anything “notable” you did as part of your process of producing this assignment-whether you did or did not use an AI tool. This should be an average of 1-3 sentences. Note that failing to meet this requirement may be met with grade penalties and you will need to provide the missing section.
*Just like the policy for plagiarism where you cannot copy and paste another’s words, ideas, data or code and represent them as your own, it is important that you write the entire assignment yourself but you can use AI tools to “assist” such as you would do with researching information on google for example (as one source of inspiration and material for your assignment).
Assignment Information
Weight:
20%
Learning Outcomes Added
LO1_ProbabilityDistribution: Given a data set or real-world situation, use standard properties of probability distributions to select appropriate distributions in one or more variables to represent the scenario.
LO2_GraphicalModels: Write down a graphical model representation of a joint probability distribution representing a statistical model (or vice versa) and manipulate the graphical notation for simplification, inference and prediction in the model.
LO3_BayesInference: Given a statistical model, use known analytical properties of standard distributions or numerical approximations for inference or prediction.
LO4_MonteCarlo: Given a statistical model, develop Monte Carlo simulations to compute approximate statistics and probability distributions for inference and prediction.
LO5_PythonImplementation: Implement rules derived using exact or approximate methods in Python to generate, summarize and present results of inference or prediction in a statistical model.
LO6_InterpretingProbabilities: Learn and practice how to summarize, interpret and communicate probability distributions and inference results.
link of workbook:
https://sle-collaboration.minervaproject.com/?id=1…
Since the answers that you would write in the workbook wouldn’t be saved, copy and paste both the questions and answers of the workbook to a Word document.
make sure to comment on each line of code
it is preferred to not use AI
