This chapter discusses the stochastic approach to price indices, exploring its historical development, methodologies, and applications. Initially proposed by Jevons and Edgeworth in the nineteenth and early twentieth centuries, this approach treats price movements as stochastic variables, reflecting uncertain fluctuations. Despite early support, the approach fell out of favor until its revival in the 1980s, driven by renewed interest in core inflation estimation and international price comparisons. The classical stochastic approach focuses on identifying common price change factors across goods, while the new stochastic approach, advanced by Selvanathan and Rao (1994), employs techniques like Generalized Least Squares to estimate price indices with standard errors. The chapter also covers criticisms of the new stochastic approach, particularly regarding the variance of residual terms, changes in estimated indices with varying sample periods, and despite these difficulties the approach has proven valuable for core inflation measurement and spatial price indices, utilizing methods such as Structural Vector Autoregression (SVAR) and Dynamic Stochastic General Equilibrium (DSGE) models. The chapter concludes by emphasizing the growing importance of the stochastic approach in econometric analysis and its potential to become a standard tool for index number theory.