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**Contents:**

Chapter 6 covers higher-order Markov chain models. Multivariate Markov models are discussed in Chapter 7.

A friendly introduction to Bayes Theorem and Hidden Markov Models

It presents a class of multivariate Markov chain models with a lower order of model parameters. Chapter 8 studies higher-order hidden Markov models. It proposes a class of higher-order hidden Markov models with an efficient algorithm for solving the model parameters. You can help correct errors and omissions.

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This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about. We have no references for this item. You can help adding them by using this form. The probabilistic model to characterize a hidden Markov process is referred to as a hidden Markov model abbreviated as HMM. The most common HMM is uni-dimensional one-dimensional , and extension to high-dimensional cases includes multi-dimensional hidden Markov model or Markov mesh random field, and more generally, Markov random field MRF , also known as Markov network.

There are a wide range of applications of HMMs to signal processing, speech recognition, character decoding, imaging analysis, economics, sociology, and life sciences [3,]. This review will briefly describe the statistical principle of HMMs and then focus on recent advances in applications of HMMs to biology and biomedicine. The hidden Markov process is a class of doubly stochastic processes, characterized by Markov property and the output independence, in which an underlying Markov process is hidden, meaning the variable states cannot be directly observed, but can be inferred through another set of stochastic processes evident as a sequence of observed outputs [11].

The Markov process generates the sequence of states of variables, specified by the initial state probabilities and state transition probabilities between variables, while the observation process outputs the measurable signals, specified by a state-dependent probability distribution, thus being viewed as a noisy realization of a Markov process. In what follows the first-order HMM is used to illustrate the theory. Since any probabilistic question about higher order Markov chains is reducible by a data augmentation device to a corresponding question about first Markov chains [6], extension of those methods to the higher order cases will be straightforward.

Without loss of generality, the time-nonhomogeneous Markov chain is used here to illustrate the statistical principle. The initial probability vector is,. HMM can be further extended to the higher dimensional cases [], and even more generally as hidden MRFs or Markov networks []. A hidden MRF is a Markov random field degraded by conditionally independent noise, of which the set of underlying variables are latent [24], often described by a graphical model for its representation of dependencies.

As shown in Figure 1C, each vertex node corresponds to a variable and the edge connecting two vertices represents a dependency.

Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present. This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises.

When there is an edge to connect every two distinct variables in a subset of vertices, such a subset is called a clique. Each clique is associated with a set of nonnegative functions, called potential functions, to specify its probability distribution. A maximal clique is a clique that cannot be extended by including one more adjacent vertex, that is, a clique which does not exist exclusively within the vertex set of a larger clique. The joint distribution of a set of variables in a MRF can be expressed by a product of several potential functions based on clique factorization,. The underlying variables are hidden but each variable will output a signal confounded with noise.

The output observation of a variable is conditionally independent of the states of other variables and the other outputs. Three fundamental problems are addressed for HMMs [11]: 1 evaluation or scoring, to compute the probability of the observation sequence for a model, 2 decoding or optimization, to find the optimal corresponding state sequence given the observation sequence and the model, 3 learning or training, to estimate the model parameters initial probabilities, transition probabilities, and emission probabilities that best explains the observation sequences given the model structure describing the relationships between variables.

Although all these computations can be implemented by the naive brute-force algorithms, which exhaustively enumerate all possible state sequences and do calculations over them for the observed series of events, they are not effective enough. The computational complexity of such algorithms increases exponentially with problem size, so that the implementation quickly becomes computationally infeasible as the number of states and the number of sequences increase, even for a small number.

The 1-D Markov model can be used to illustrate this point. When moving from a variable to the next along the Markov chain, each current state may shift to one of the possible next states, and then a state transition diagram can be formed by connecting all plausible moves, as shown in Figure 2A.

Powerful algorithms can be developed through recursive factorization or dynamic programming by making use of the conditional independence given the Markov blanket of a variable or a group of variables, so that substantial redundant calculations and storage are avoided, and much fewer arithmetic operations and less computing resources such as computer memory are required.

The principle of trellis algorithm is extensively used in statistical analysis for 1-D hidden Markov models. As visualized in Figure 2, the states in various variables are arranged into a trellis according to the order of variables, whose dimensions are, respectively, the number of states and the length of the variable sequence.

The nodes at a vertical slice represent the states of the corresponding variable.

Computational times in seconds. Furthermore, the Markov chain model can also be used in a generative mode to automatically obtain tours. Published by Springer US This latter theoretical motif frequency is subsequently compared with the frequency observed in the real sequence. The system then suggests to the user items that other users with similar interests have purchased e. Moreover, PBN seems to be unable to model a set of genes when n is quite large. We can estimate the con d ditional probability distribution Xi1 ,

By use of the conditional independency, the intermediate calculations on all the paths in which a node is involved can be individually cached to avoid redundant calculation. The forward-backward algorithm [25,26], the Viterbi algorithm [], and the Baum-Welch algorithm a special type of Expectation-Maximization, abbreviated by EM, algorithm [30,31], were developed based on the trellis diagram and can used for purposes of evaluation, decoding, and learning, respectively. The relevant theory and methods is concisely recapitulated as follows. Both the forward algorithm and the backward algorithm involve three steps: initialization, recursion or induction , and termination.

The Viterbi algorithm consists of four steps: initialization, recursion, termination, and backtracking. Given a set of examples from a sequence and the HMM structure, the EM algorithm can be implemented for model fitting. The EM algorithm is an iterative method to find the maximum likelihood estimate s MLE based on the following principle [32]: From the Kullback-Leibler divergence theory [33], the expected log-likelihood function of the complete data consisting of the observed data, known as incomplete data, and the unobserved latent data is a lower bound on the log-likelihood of the observed data, that is, the log-likelihood function of the complete data under any set of probability distribution of hidden data is less than or equal to the log-likelihood of the observed data.

Therefore, we can use the expected log-likelihood function of the complete data as a working function, iteratively approaching to the log-likelihood of the observed data, the true objective function, and thereby finding the MLE. The EM algorithm alternate between performing an expectation step E-step and a maximization step M-step. In an E-step, the expectation of the log-likelihood of complete data is evaluated using the current estimate for the parameters to create a function for maximization, and in an M-step, the parameters maximizing the expected log-likelihood found in the E-step are computed.

It can be proved that such an iteration will never decrease the objective function, assuring the EM converges to an optimum of the likelihood. The EM algorithm will be very efficient in particular for the cases when there is the closed-form solution to MLE for the complete data. The EM algorithm includes three steps of initialization, a series of iterations, and termination. Each cycle of EM iteration involves two steps, an E-step followed by an M-step, alternately optimizing the log-likelihood with respect to the posterior probabilities and parameters, respectively.

Further, calculate the posterior state probabilities of a variable and of the state combinations of two adjacent variables as follows,. Then, the function for the expectation of the log-likelihood of complete data is computed by using the estimated posterior probabilities of hidden data. M-step: Estimate the new parameters that maximize the expected log-likelihood found in the E-step.

To perform the M-step will assure that the likelihood of complete data computed from the E-step will be maximized with respect to the parameters. Repeat the E-step and the M-step until convergence is reached such as the objective function does not increase or the parameter estimation does no longer change. In practice, such an extension is not easy to implement. One solution is to convert a multi-dimensional model into a 1-D multi-dimensional vector Markov model by considering the set of nodes with a fixed coordinate along a given direction e.

For example, the rows, the columns, and the anti-diagonal and its parallelisms of a 2-D lattice respectively form super nodes, generating a vector Markov chain []. Generalization to the 3-D case is also straightforward []. The main limitation of these approaches is that, although avoiding exhaustive computations along the chosen dimension, it is still necessary to consider all the possible combinations of states in the resulting vector and thus the complexity is lessened only from one direction; in other words, the computational complexity of the algorithms grows exponentially with the data size in the other dimension s , e.

Alternatively, restricted models with reduced connectivity such as pseudo multi-dimensional HMMs [44,45], embedded HMMs [46, 47], dependence-tree HMMs [48] are suggested for use. One of the major shortcomings is that the dependence of a node on its neighbors in a fully connected multi-dimensional HMM does not guarantee to be explored.

Several attempts have also been done to heuristically reduce the complexity of the HMM algorithms by making simplifying assumptions [34,36,42,43,].

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The main disadvantage of these approaches is that they only provide approximate computations, such that the probabilistic model is no longer theoretically sound. On hidden MRFs, there are also a few methods applicable to exact computation such as variable elimination methods an analogue to the forward-backward algorithm and belief propagation methods sum-product message passing and max-product message passing methods corresponding to the forward-backward and the Viterbi algorithms, respectively [53]. It is believed that there exists no general method efficiently for exact computation of hidden MRFs and higher dimensional HMMs.

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In most cases, exact inference is not allowed because of tremendous computational burden. Several approximate inference approaches and numerical computing techniques such as Markov chain Monte Carlo sampling [54], variational inference [55], and loopy belief propagation [56,57] methods are more feasible. My research team, funded by an NSF grant, is developing a telescopic algorithm which can make full use of the property of conditional independence in the models and is expected to increase the computational complexity linearly rather than exponentially with the sizes of both dimensions in a 2-D HMM, thus greatly lowering the cost in computing resources including computer memory and computing time and being applicable to exact computation in an iterative way for statistical inference of high dimensional HMMs.

viptarif.ru/wp-content/numbers/888.php HMMs offer an effective means for prediction and pattern recognition in biological studies.

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