How Is the Distribution of B_s Given B_t Computed in Brownian Motion?

AI Thread Summary
The distribution of B_s given B_t in standard Brownian motion is computed as a normal distribution for 0 ≤ s < t. The discussion highlights the need for precise phrasing in describing this computation, suggesting it can be framed as the distribution of B_s over σ(B_t). It emphasizes the importance of partitioning distributions to avoid overlap. Utilizing properties of Wiener processes provides sufficient justification for the computation. Overall, the conversation centers on accurately articulating the statistical relationships in Brownian motion.
IniquiTrance
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I computed the distribution of B_s given B_t, where 0\leq s &lt;t and \left\{B_t\right\}_{t\geq 0} is a standard brownian motion. It's normal obviously..

My question is, how do I phrase what I've done exactly? Is it that I computed the distribution of B_s over \sigma(B_t)?
 
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Hey IniquiTrance.

If you partition the distributions so that they don't overlap then you can use the properties of a Wiener (or Brownian motion) process and that should be enough in terms of the justification used.
 
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