Diffusion Models for Time Series: Review the Model

Topics: Review the model Use the following timezone tool or click on the “Add to Calendar” button on the sidebar. Click here for an interactive widget.

Current Topic

## When and How

The discussions are hosted online in Lark/Wechat.

• Lark is our primary communication channel.
• Join the group using • Wechat is mostly for our backup plans.

If you would like to be part of the party, please create a post here on GitHub discussions.

The discussions are mostly in Chinese.

### When

This is a bi-weekly meetup.

There are two different ways to keep track of the upcoming events:

2. If you would like to add individual events by yourself, use the “Add to Calendar” button on the specific event page.
• Here is the button:

As a preview of the events, here is a calendar web page for the upcoming events (Calendar Page):

### Rules

• Everyone shall get their chance to lead the discussion.
• The first principle is to understand the content. Interrupt and ask any questions to make sure we all understand the content well.

## Why this Topic

Conditional probability estimation is one of the most fundamental problems in statistics.

• Conditional probability estimation is frequently used in solving both real life and academic problems. One is likely to encounter this problem at some point of their life.
• If you are inferring, you are probably using conditional probabilities. It is a perspective.
• There are many models and methods to estimate the conditional probability. We can learn about and from these models and methods.
• We need a universal model to solve this problem for productivity. A universal model for this task will save us a lot of time and energy.
• Many machine learning methods are based on conditional probabilities.
• Many classifiers
• Bayesian networks

## What is Our Approach

• Apply on toy problems

We will update this list on our way forward. Here is a partial list of references.

Initial Proposal (Outdated)
As a start this is an outline of what should be covered.

• What is the conditional probability?
• Sampling theory
• Bayes
• Representation of a conditional probability
• Statistical methods to estimate the conditional probability
• The list is enormous. We will only concentrate on the basics.
• Tree-based
• Tree as “clustering” method
• Application on the bike-sharing problem
• NN-based
• NN as feature transformations
• Application on the bike-sharing problem
• EM Methods
• Variational Methods
• Normalizing Flow