Identification of signals under statistical uncertainty

Key Points

• This study aims to explore decision-making when accepting one of two hypotheses amid random noise using Bayesian risk.
• Analyzed the problem of hypothesis acceptance with known prior probabilities
• Adopted Bayesian risk as the objective function
• Considered particular cases to understand decision-making under uncertainty
• Identified optimal decision rules that minimize Bayesian risk
• Enhanced understanding of hypothesis acceptance in noisy environments
• Revealed implications for statistical inference processes