Sources of Technical Risks: Beta Distribution Concept

Sources of Technical Risks
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Sources of Technical Risks
The Beta distribution constitutes a continuous probability distribution typically employed to model the uncertainty concerning the experiment’s success probability. Probability is anchored on the possibility of something happening. Theoretical probability is primarily centered on the reasoning behind probability. Whenever unsure about the event outcome, we might talk about the possibilities of specific outcomes – how likely they are. The program evaluation and review technique (PERT) distribution (also referred to as the three-point estimation technique or beta-POINT) is theorized as a smooth triangular distribution or uniform distribution. The Three-Point Estimation technique is centered on Beta Distribution and employs three distinct duration estimates (optimistic, pessimistic, and most likely) to derive the ultimate distribution. PERT mitigates the risks and augments the estimation accuracy using a weighted average (Sherman, n.d). This paper discusses the beta distribution concept, when it is employed, the shortcomings and benefits of the approach, and the result of the estimate in the example.
The Beta distribution is applied to model things with a limited range, including zero to one. Examples constitute the probability of success within an experiment demonstrating only two results –to fail or succeed. The beta distribution was traditionally employed in different disciplines as a bounded distribution approach to the normal distribution. Its application was proposed in the context of data unavailability based on only information offered by the expert regarding the optimistic, pessimistic, and most likely values (Sivaprasad & MacKenzie, 2018). Initially proposed in the context of PERT, this approach was widely used in crucial projects, including the Concorde plane, and remains a recommended methodology in project management contexts. It is used in project management to help establish uncertainty in task time estimation. It is used in collaboration with fundamental statistics to offer practical quantitative instruments to manage the anticipated time to complete the project.
One of the primary benefits of beta distribution methodology is its flexibility of the density function shape. Incorporating beta distribution in the existing distributional alternatives within the software for growth mixture modeling constitutes a valuable improvement of the extant distribution alternatives. Therefore, the beta distribution offers an alternative to the censored normal distribution to model continuous or roughly continuous evaluated outcomes evaluated over time or age (Elmer et al., 2018). Whereas it is a flexible and versatile technique in providing a good description of various data types, the beta distribution is not without shortcomings. Using beta is based on historical data and might accurately predict rapi...