<p>Opponent models that predict opponents’ utility functions can help achieve favorable outcomes in automated bilateral multi-issue negotiations. Bayesian learning-based opponent models are flexible and adaptable to various negotiation contexts. However, existing Bayesian learning-based opponent models compromise prediction accuracy for computational efficiency by assuming independent issues and specific utility function shapes. We propose a novel Bayesian learning-based opponent model that improves prediction accuracy while maintaining computational efficiency by relaxing the shape assumption and separately learning each parameter of the utility function. Although parameters are estimated independently, this removes structural constraints and increases mutual dependence between parameters during inference. Each parameter is estimated with its conditional expectation, conditioned on the estimates of the other parameters, and computed efficiently through an iterative learning algorithm. We further introduce a resampling method to mitigate degeneracy in the hypothesis space and maintain diversity. Experiments across 45 negotiation domains against seven temporal and 10 Automated Negotiating Agents Competition (ANAC) final-list agents show that the proposed model outperforms existing Bayesian learning-, frequency-, and value-based opponent models. Ablation results validate the effectiveness and synergy of the parametric interrelation consideration and the resampling method.</p>