Reestimation

Reestimation is the process of revising previous estimates in light of new information. It is used across disciplines to improve the accuracy of quantities such as time, cost, demand, or model parameters that were initially estimated. Reestimation can be prompted by changes in scope, updated data, new model assumptions, or observed performance that differs from expectations.

In project management and software development, reestimation updates baselines for effort, duration, and budgets. It commonly follows scope changes, completed work that deviates from plans, or the arrival of new requirements. Reestimation aims to reflect current expectations, inform decision making, and may lead to re-baselining, revised schedules, and adjusted resource allocations. Effective governance and transparent communication are important to manage the impact on stakeholders and commitments.

In statistics and forecasting, reestimation refers to updating parameter values as additional data become available. Methods include Bayesian updating, maximum likelihood reestimation, and iterative optimization. In machine learning and dynamic models, reestimation supports adaptive learning, online updating, and calibration when data distributions change. The reestimation step in algorithms such as the expectation-maximization procedure updates model parameters given new information.

Considerations for reestimation include data quality, timing, and frequency of updates; potential biases or overfitting from frequent changes; and the need for documentation, version control, and clear decision criteria to avoid excessive volatility in plans and forecasts.