Variational Inference

Variational Inference is a statistical technique used in Bayesian inference to approximate complex probability distributions. It transforms the problem of inference into an optimization problem by introducing a simpler, parameterized distribution and then finding the parameters that minimize the difference between this distribution and the true posterior distribution. This approach allows for efficient computation in scenarios where exact inference is intractable.

Articles in this topic

  • What is Variational Inference?

    Variational inference is a technique in machine learning used for approximating complex probability distributions. It transforms inference problems into optimization problems, making it easier to handle high-dimensional data.

  • How does Variational Inference work?

    Variational inference works by approximating a complex posterior distribution with a simpler distribution through optimization. It involves defining a family of distributions and adjusting their parameters to minimize the difference from the true distribution.

  • Use Cases of Variational Inference

    Variational inference is widely used in various applications, including Bayesian data analysis, machine learning, and statistical modeling. Its ability to handle high-dimensional data makes it particularly valuable.