Represent and manipulate observed data into predictions

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Reference no: EM133548657

Question: It is important for developers to learn to represent and manipulate observed data into predictions.

Choose an aspect of a game or simulation in which a hidden Markov model, a Kalman filter, or a dynamic Bayesian network is an essential components. Adapt the concept of probabilistic learning. Then, include the following:

  1. A brief description of the game or simulation (one paragraph).
  2. A brief description of the search methods that will be used (one paragraph and bulleted outline).
  3. A flowchart detailing the logic of the algorithm(s) implemented.
  4. The entire code implemented for this project. If this is not part of bigger project, state this explicitly. If it is part of a bigger project and the entire parent project is needed to run it, include the parent project. The project may be too large to upload to the digital platform. If this is the case, upload it to your selected repository only and share the link with the instructor. Note: You will need to submit the code to your selected repository anyway (to include all source files with well commented code).

Reference no: EM133548657

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