Conditional Probability Estimation

An online discussion group on the topic Conditional Probability Estimation

Introduction: Conditional Probability Estimation

8 MAF: how is MADE being used

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Summary: We discussed MAF (arXiv:1705.07057v4) last time: The paper did not explain how exactly is MADE being used to update the shift and logscale. We will use the tensorflow implementation of MAF to probe the above question. Here is the link to the relevant documentation: https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors/MaskedAutoregressiveFlow Topics Refer to references. Notes 1310.8499_notes.pdf
Pages: 24

7 MADE: Masked Autoencoder for Distribution Estimation

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Summary: Topics Refer to references. Notes 1310.8499_notes.pdf
Pages: 24

5 Review of Normalizing Flow

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Summary: Topics Normalizing flow Applications of normalizing flow Methods of normalizing flow Problems of normalizing flow
Pages: 24

3 EM Methods

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Summary: EM method, expectation-maximization algorithm, is an inspiring iterative method to find the log-likelihood by introducing some intermediate variable such as responsibility.
Pages: 24