Hidden state models improve state-dependent diversification approaches, including biogeographical models.

A hidden Markov model (HMM) has been widely used as a cost-effective solution for time series based applications such as hand gesture recognition. A large number of parameters in an HMM often result in a degradation of classification accuracy due to overfitting. Furthermore, the accuracy is often affected by the initialization values of the parameters. Left-right banded (LRB) HMM is one of the representative models which improve the classification accuracy by reducing the number of parameters. However, LRB HMM still suffers from false negative misclassification when it is applied for hand gesture recognition. Misclassification in hand gesture recognition arise from similar trajectories of multiple gestures that are represented by hidden states. This paper proposes a new initialization process that reduces not only state transition parameters but also hidden state observation parameters. To this end, the gesture trajectory is predicted and then the parameters are masked out when their probabilities are negligibly small in the predicted trajectory. Experiment results show that the confusion rate is nearly 14% lower than the LRB HMM while maintaining the classification accuracy.

Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California

Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable. We present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the other data series and estimate their effects on the odds of each transition in the given series. Following this, hidden states are inferred using a standard forward-backward algorithm, with the transition probabilities adjusted by the model at each position, which helps retain the order of computation close to fitting independent HMMs (iHMM). Hence, scHMM is a collection of inter-dependent non-homogeneous HMMs, capable of giving a close approximation to a fully multivariate HMM fit. A simulation study shows that scHMM achieves comparable sensitivity to the multivariate HMM fit at a much lower computational cost. The method was demonstrated in the joint analysis of 39 histone modifications, CTCF and RNA polymerase II in human CD4+ T cells. scHMM reported fewer high-confidence regions than iHMM in this dataset, but scHMM could recover previously characterized histone modifications in relevant genomic regions better than iHMM. In addition, the resulting combinatorial patterns from scHMM could be better mapped to the 51 states reported by the multivariate HMM method of Ernst and Kellis. The scHMM package can be freely downloaded from http://sourceforge.net/p/schmm/ and is recommended for use in a linux environment.

Eye-tracking allows researchers to infer cognitive processes from eye movements that areclassified into distinct events. Parsing the events is typically done by algorithms. Here weaim at developing an unsupervised, generative model that can be fitted to eye-movementdata using maximum likelihood estimation. This approach allows hypothesis testing aboutfitted models, next to being a method for classification. We developed gazeHMM, an algorithmthat uses a hidden Markov model as a generative model, has few critical parametersto be set by users, and does not require human coded data as input. The algorithm classifiesgaze data into fixations, saccades, and optionally postsaccadic oscillations and smooth pursuits.We evaluated gazeHMM’s performance in a simulation study, showing that it successfullyrecovered hidden Markov model parameters and hidden states. Parameters wereless well recovered when we included a smooth pursuit state and/or added even small noiseto simulated data. We applied generative models with different numbers of events to benchmarkdata. Comparing them indicated that hidden Markov models with more events thanexpected had most likely generated the data. We also applied the full algorithm to benchmarkdata and assessed its similarity to human coding and other algorithms. For static stimuli,gazeHMM showed high similarity and outperformed other algorithms in this regard. Fordynamic stimuli, gazeHMM tended to rapidly switch between fixations and smooth pursuitsbut still displayed higher similarity than most other algorithms. Concluding that gazeHMMcan be used in practice, we recommend parsing smooth pursuits only for exploratory purposes.Future hidden Markov model algorithms could use covariates to better capture eyemovement processes and explicitly model event durations to classify smooth pursuits moreaccurately.

Informatics resources for the Collaborative Cross and related mouse populations.

In fact, millions of people in the world speak many languages. In order to communicate with each other, it is necessary to know the language we use to perform this operation. The Arabic language has many different dialects and it must be recognized before using the automatic speech recognition (ASR). On the other hand, it is observed in all Arab countries that the standard Arabic language is widely written and used in an official speech, newspapers, public administration, and schools but it is not used in the daily conversations instead the dialect is widely spoken in daily life and rarely written. In this paper, we examine the difficult task of properly identifying various Arabic dialects and propose a system developed to identify a set of four regional and modern standard Arabic speeches, based on speech recognition using Hidden Markov Models (HMMs) algorithms. HMMs have become a very popular way to build a speech recognition system. It is set as hidden states and possibilities of transition from one state to another. Due to the similarities and differences between the Arabic dialects, speeches collected from the ADI5 datasets were retrieved from the MGB-3 challenge source. We proposed an Arabic Dialect Identification System called "Building a System for Arabic Dialects Identification based on Speech Recognition using Hidden Markov Models (HMMs)" that takes Input as speech utterances and produces output as dialect being spoken. During the training phase, speech utterances from one or more dialects were analyzed to capture the important properties of audio signals in terms of time and frequency. During the testing phase, previously unseen test utterances were utilized to the system, and the system outputs the dialect associated with the model of dialect that most closely matches the test utterance. The proposed model of the system shows promising results of the model for each dialects match.

The distribution of diversity can vary considerably from clade to clade. Attempts to understand these patterns often employ state-dependent speciation and extinction models to determine whether the evolution of a particular novel trait has increased speciation rates and/or decreased extinction rates. It is still unclear, however, whether these models are uncovering important drivers of diversification, or whether they are simply pointing to more complex patterns involving many unmeasured and co-distributed factors. Here we describe an extension to the popular state-dependent speciation and extinction models that specifically accounts for the presence of unmeasured factors that could impact diversification rates estimated for the states of any observed trait, addressing at least one major criticism of BiSSE (Binary State Speciation and Extinction) methods. Specifically, our model, which we refer to as HiSSE (Hidden State Speciation and Extinction), assumes that related to each observed state in the model are "hidden" states that exhibit potentially distinct diversification dynamics and transition rates than the observed states in isolation. We also demonstrate how our model can be used as character-independent diversification models that allow for a complex diversification process that is independent of the evolution of a character. Under rigorous simulation tests and when applied to empirical data, we find that HiSSE performs reasonably well, and can at least detect net diversification rate differences between observed and hidden states and detect when diversification rate differences do not correlate with the observed states. We discuss the remaining issues with state-dependent speciation and extinction models in general, and the important ways in which HiSSE provides a more nuanced understanding of trait-dependent diversification.

This paper examines the quarterly corn production dynamics in the Philippines between 2010 and 2025 through a Hidden Markov Model (HMM) approach to determine hidden regimes of volatility. Through the modeling of production as a process driven by hidden states, the estimation delivers an evident switch between low- and high-volatility regimes, each with different mean levels and residual variances. Stationarity testing upheld the applicability of the time series to regime modeling, and model comparison with AIC, BIC, and log-likelihood statistics highly preferred the 2-state HMM to a reduced 1-state model. Transition probability estimates reflected almost deterministic switching behavior between regimes and demonstrated the cyclical pattern of agricultural production, presumably caused by seasonal, climatic, or policy-based factors. The results highlight the usefulness of regime-switching models in identifying latent structural change in farm-level data and facilitate their use in prediction, risk management, and policy formulation towards improved food security, aiding the achievement of UN Sustainable Development Goal 2 (Zero Hunger).

Antibiotic-resistant nosocomial pathogens can arise in epidemic clusters or sporadically. Genotyping is commonly used to distinguish epidemic from sporadic vancomycin-resistant enterococci (VRE). We compare this to a statistical method to determine the transmission characteristics of VRE. A structured continuous-time hidden Markov model (HMM) was developed. The hidden states were the number of VRE-colonized patients (both detected and undetected). The input for this study was weekly point-prevalence data; 157 weeks of VRE prevalence. We estimated two parameters: one to quantify the cross-transmission of VRE and the other to quantify the level of VRE colonization from sporadic sources. We compared the results to those obtained by concomitant genotyping and phenotyping. We estimated that 89% of transmissions were due to ward cross-transmission while 11% were sporadic. Genotyping found that 90% had identical glycopeptide resistance genes and 84% were identical or nearly identical on pulsed-field gel electrophoresis (PFGE). There was some evidence, based on model selection criteria, that the cross-transmission parameter changed throughout the study period. The model that allowed for a change in transmission just prior to the outbreak and again at the peak of the outbreak was superior to other models. This model estimated that cross-transmission increased at week 120 and declined after week 135, coinciding with environmental decontamination. We found that HMMs can be applied to serial prevalence data to estimate the characteristics of acquisition of nosocomial pathogens and distinguish between epidemic and sporadic acquisition. This model was able to estimate transmission parameters despite imperfect detection of the organism. The results of this model were validated against PFGE and glycopeptide resistance genotype data and produced very similar results. Additionally, HMMs can provide information about unobserved events such as undetected colonization.

Optical Coherence Tomography (OCT) is a useful imaging modality facilitating the capturing process from retinal layers. In the salient diseases of retina, cysts are formed in retinal layers. Therefore, the identification of cysts in the retinal layers is of great importance. In this paper, a new method is proposed for the rapid detection of cystic OCT B-scans. In the proposed method, a Hidden Markov Model (HMM) is used for mathematically modelling the existence of cyst. In fact, the existence of cyst in the image can be considered as a hidden state. Since the existence of cyst in an OCT B-scan depends on the existence of cyst in the previous B-scans, HMM is an appropriate tool for modelling this process. In the first phase, a number of features are extracted which are Harris, KAZE, HOG, SURF, FAST, Min-Eigen and feature extracted by deep AlexNet. It is shown that the feature with the best discriminating power is the feature extracted by AlexNet. The features extracted in the first phase are used as observation vectors to estimate the HMM parameters. The evaluation results show the improved performance of HMM in terms of accuracy.

Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general 2-part coding scheme for given model order but unknown parameters based on PHMM is presented. A forward-backward reestimation of parameters with a redefined backward variable is given for these models and used for estimating the unknown parameters. Proof of convergence of this reestimation is given. The PHMM structure and the conditions of the convergence proof allows for application of the PHMM to image coding. Relations between the PHMM and hidden Markov models (HMM) are treated. Results of coding bi-level images with the PHMM coding scheme is given. The results indicate that the PHMM can adapt to instationarities in the images.

High-order Hidden Markov Model for trend prediction in financial time series

Önemli yatırım araçlarından biri olan altın, piyasalar üzerindeki etkisi sebebiyle dünya çapında birçok yatırımcı tarafından takip edilmektedir. Bu sebeple altın fiyatlarının tahmini ve etkileyen faktörlerin belirlenmesi önemli problemlerden biridir. Bu çalışmada, altın fiyatlarındaki yüzdesel değişim oranı ve sebepleri saklı Markov modelleri ile tahmin edilmiştir. Bunun için 2013-2023 yılları arası altın, dolar, faiz ve enflasyon oranı verileri kullanılmıştır. Altın fiyatlarındaki yüzdesel değişim gözlemler olarak tanımlanırken, altın fiyatlarını etkileyen dolar, faiz ve enflasyon oranlarından mümkün 18 saklı durum elde edilmiştir. Saklı Markov modeli birinci temel probleminin çözümünde ileri yön algoritması kullanılmıştır. Bu algoritma ile 2 aylık dönemlerde karşılaşılacak gözlem dizisi olasılıkları hesaplanmıştır. Bu dönemde altın fiyatlarındaki yüzdesel değişimin 0,5’ten büyük olması olasılığı %23,6 olarak bulunmuştur. Gözlem dizilerini en iyi açıklayan saklı durumlar Viterbi algoritması ile incelenmiştir. Elde edilen sonuçlar gerçek veriler ile karşılaştırdığında yüksek doğruluk oranlarının elde edildiği gözlenmiştir.

In standard automatic speech recognition (ASR), hidden Markov models (HMMs) calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned only upon the hidden state variable. Stephenson et al. (2001) showed the benefit of conditioning the emission distributions also upon a discrete auxiliary variable, which is observed in training and hidden in recognition. Related work (Fujinaga et al., 2001) has shown the utility of conditioning the emission distributions on a continuous auxiliary variable. We apply mixed Bayesian networks (BNs) to extend these works by introducing a continuous auxiliary variable that is observed in training but is hidden in recognition. We find that an auxiliary pitch variable conditioned itself upon the hidden state can degrade performance unless the auxiliary variable is also hidden. The performance, furthermore, can be improved by making the auxiliary pitch variable independent of the hidden state.

This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Nino–Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Nino 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Nino–Southern Oscillation in Peninsular Malaysia.