# Bayesian Linear Regression and Maximum a Posteriori (MAP) Estimate

## Метаданные

- **Канал:** Steve Brunton
- **YouTube:** https://www.youtube.com/watch?v=wdWHbYdhfG8
- **Дата:** 24.04.2026
- **Длительность:** 15:16
- **Просмотры:** 6,410

## Описание

In this video we show how to incorporate prior information into the least squares regression, consistent with the framework of Bayesian statistics.  The so-called maximum a posteriori (MAP) estimate is one of the foundational tools in statistical fitting and machine learning. 

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

## Содержание

### [0:00](https://www.youtube.com/watch?v=wdWHbYdhfG8) Segment 1 (00:00 - 05:00)

e min for on should for e of e right

### [5:00](https://www.youtube.com/watch?v=wdWHbYdhfG8&t=300s) Segment 2 (05:00 - 10:00)

all e for um for how e this

### [10:00](https://www.youtube.com/watch?v=wdWHbYdhfG8&t=600s) Segment 3 (10:00 - 15:00)

and for fire to for e

---
*Источник: https://ekstraktznaniy.ru/video/52884*