Variational Inference Concepts

The video introduces variational inference, focusing on the challenges of working with posterior distributions over latent variables. It explains the use of surrogate posteriors to approximate complex distributions and introduces the evidence lower bound (ELBO) as a key concept. The video discusses optimization techniques using KL divergence and ELBO, providing an interactive example to illustrate these concepts. The goal is to perform inference on latent variables by optimizing over potential distributions, ultimately maximizing the ELBO to approximate the true posterior.

What is the main challenge in finding the posterior distribution over a latent variable set?

• Lack of data

• Complexity of the closed form

• Insufficient computational power

• Inaccurate data collection

What is the purpose of using a surrogate posterior in variational inference?

• To simplify the computation

• To increase data accuracy

• To reduce data size

• To eliminate noise

In the context of directed graphical models, what do the observed variables represent?

• Noise

• Data set

• Random variables

• Latent variables

What is the goal of the optimization problem in variational inference?

• To minimize the KL divergence

• To find the maximum likelihood

• To reduce computational time

• To maximize the data set

What does the Kullback-Leibler divergence measure?

• The variance of a distribution

• The accuracy of a model

• The distance between two distributions

• The similarity between two data sets

What is the evidence lower bound (ELBO) used for in variational inference?

• To increase computational speed

• To bound the evidence from below

• To measure data accuracy

• To reduce data noise

In the interactive example, what happens when the KL divergence is minimized?

• The evidence increases

• The data set size increases

• The surrogate posterior becomes more accurate

• The ELBO decreases