论文标题
平行回火作为促进分层隐藏模型推断的机制
Parallel tempering as a mechanism for facilitating inference in hierarchical hidden Markov models
论文作者
论文摘要
近年来，通过隐藏的马尔可夫模型和类似的状态切换模型推断出的动物行为状态的研究显着提高。通过分层隐藏的马尔可夫模型，可以考虑到不同行为量表的能力，但是额外的水平会导致更高的复杂性和增加模型组件之间的相关性。使用EM算法进行推理的最大似然方法和可能的直接优化，因此更频繁地使用了可能性的可能性，由于计算需求，贝叶斯方法不太受青睐。鉴于这些需求，至关重要的是，在首选贝叶斯方法时，开发有效的估计算法。我们研究了各种方法来改善收敛时间和在马尔可夫链中混合的蒙特卡洛方法，该方法应用于分层隐藏的马尔可夫模型，包括平行回火作为推理促进机制。该方法显示了分析组件之间具有高度相关性的复杂随机模型的希望，但是我们的结果表明，它需要仔细调整以最大程度地发挥潜力。
The study of animal behavioural states inferred through hidden Markov models and similar state switching models has seen a significant increase in popularity in recent years. The ability to account for varying levels of behavioural scale has become possible through hierarchical hidden Markov models, but additional levels lead to higher complexity and increased correlation between model components. Maximum likelihood approaches to inference using the EM algorithm and direct optimisation of likelihoods are more frequently used, with Bayesian approaches being less favoured due to computational demands. Given these demands, it is vital that efficient estimation algorithms are developed when Bayesian methods are preferred. We study the use of various approaches to improve convergence times and mixing in Markov chain Monte Carlo methods applied to hierarchical hidden Markov models, including parallel tempering as an inference facilitation mechanism. The method shows promise for analysing complex stochastic models with high levels of correlation between components, but our results show that it requires careful tuning in order to maximise that potential.
