On the Use of Local Optimizations within Metropolis–Hastings Updates
在 Metropolis–Hastings 更新中使用局部优化
Summary We propose new Metropolis–Hastings algorithms for sampling from multimodal dis- tributions on ℜn. Tjelmeland and Hegstad have obtained direct mode jumping proposals by optimization within Metropolis–Hastings updates and different proposals for ‘forward’ and ‘backward’ steps. We generalize their scheme by allowing the probability distribution for forward and backward kernels to depend on the current state. We use the new setting to combine mode jumping proposals and…