隐藏的马尔可夫模型的下一个状态仅取决于上一个状态?那以前的n个州呢? [英] Hidden markov model next state only depends on previous one state? What about previous n states?

问题描述

我正在研究原型框架.

基本上，我需要基于每个人的一些传感器数据(例如GPS，运动，心率，周围环境读数，温度等)为每个人的生活方式生成模型或配置文件.

建议的模型或配置文件是个人生活方式的知识表示.也许是带有概率的图.

我正在考虑使用隐马尔可夫模型来实现此目的.由于HMM中的状态可以是工作，睡眠，休闲，运动等.观测值可以是各种传感器数据的集合.

我对HMM的理解是，下一个状态S(t)仅取决于前一个状态S(t-1).但是实际上，一个人的活动可能取决于先前的n个州.使用HMM还是个好主意吗?还是应该使用其他一些更合适的模型?我已经看到了一些关于二阶和多阶马尔可夫链的工作，它也适用于HMM吗?

如果您能给我详细的解释，我非常感谢.

谢谢！

解决方案

您正在谈论的是一阶HMM，其中您的模型仅了解先前的历史状态.如果是阶n马尔可夫模型，则下一个状态将取决于先前的"n"个状态，也许这就是您要寻找的对吗?

您认为对简单HMM而言，下一个状态仅取决于当前状态是正确的.但是，也可以通过定义过渡概率来实现第m阶HMM，如在此链接中.但是，随着阶数的增加，矩阵的整体复杂性也随之增加，因此模型也随之增加，因此，如果您要应对挑战并愿意付出必要的努力，则取决于您.

I am working on a prototype framework.

Basically I need to generate a model or profile for each individual's lifestyle based on some sensor data about him/her, such as GPS, motions, heart rate, surrounding environment readings, temperature etc.

The proposed model or profile is a knowledge representation of an individual's lifestyle pattern. Maybe a graph with probabilities.

I am thinking to use Hidden Markov Model to implement this. As the states in HMM can be Working, Sleeping, Leisure, Sport and etc. Observations can be a set of various sensor data.

My understanding of HMM is that next state S(t) is only depends on previous one state S(t-1). However in reality, a person's activity might depends on previous n states. Is it still a good idea to use HMM? Or should I use some other more appropriate models? I have seen some work on second order and multiple order of Markov Chains, does it also apply HMM?

I really appreciate if you can give me a detailed explanation.

Thanks!!

解决方案

What you are talking about is a First Order HMM in which your model would only have knowledge of the previous history State. In case of an Order-n Markov Model, the next state would be dependent on the previous 'n' States and may be this is what you are looking for right?

You are right that as far as simple HMMs are considered, the next state is dependent only upon the current state. However, it is also possible to achieve a mth Order HMM by defining the transition probabilities as shown in this link. However, as the order increases, so does the overall complexity of your matrices and hence your model, so it's really upto you if your up for the challenge and willing to put the requisite effort.

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