Bayesian analysis provides a disciplined way to update uncertainty when new evidence arrives. It begins with a prior distribution, specifies how the observed data would arise under competing assumptions and produces a posterior distribution. In markets, its value is not that it turns judgement into certainty; it makes assumptions visible and lets forecasts, parameters and decision rules carry uncertainty forward.
For example, say a trader has a hypothesis about market trends. Based on experience, they assign a 40% chance the trend will continue. After more price action, their belief either strengthens or weakens. Bayesian analysis gives a calculated way to revise that initial probability based on what the new data shows. This iterative process captures how knowledge grows over time as more facts emerge.