Explore the vast range of titles available.

A Markov tree is a random vector indexed by the nodes of a tree whose distribution is determined by the distributions of pairs of neighbouring variables and a list of conditional independence relations. Upon an assumption on the tails of the Markov kernels associated to these pairs, the conditional distribution of the self-normalized random vector when the variable at the root of the tree tends to infinity converges weakly to a random vector of coupled random walks called a tail tree. If, in addition, the conditioning variable has a regularly varying tail, the Markov tree satisfies a form of one-component regular variation. Changing the location of the root, that is, changing the conditioning variable, yields a different tail tree. When the tails of the marginal distributions of the conditioning variables are balanced, these tail trees are connected by a formula that generalizes the time change formula for regularly varying stationary time series. The formula is most easily understood when the various one-component regular variation statements are tied up into a single multi-component statement. The theory of multi-component regular variation is worked out for general random vectors, not necessarily Markov trees, with an eye towards other models, graphical or otherwise.

Background Men who have sex with men (MSM) globally face a high risk of HIV infection. Previous studies indicate that customized short message service (SMS) interventions could reduce high-risk behaviors that associated with HIV transmission. This study aims to evaluate the health and economic impacts of such interventions among MSM in China. Methods A decision tree-Markov model was developed for a simulated cohort of 100,000 MSM of 20 years old. We assessed three intervention strategies: (1) routine strategy with standard health information; (2) SMS strategy with customized messages based on individual high-risk behaviors, with 50.1% efficacy and 50% coverage; (3) LEN-LA (lenacapavir long-acting) strategy as pre-exposure prophylaxis (PrEP), with 100% efficacy lasting for 0.5-year and 50% coverage. The study period was 45 years. Primary outcomes included the number of HIV infections and HIV-related deaths. The cost-effectiveness, cost-utility and cost-benefit analyses were conducted along with sensitivity analyses from the healthcare sector perspective. Results The SMS strategy was more effective, averting 6,191 (22.0%) HIV infections and 2,100 (38.5%) HIV-related deaths when compared with routine strategy. The average cost-effectiveness ratios (ACERs) were US$6,361 (95% CI: 5,959-6,613) per HIV infection averted and US$18,752 (95% CI: 17,274 − 20,530) per HIV-related death averted. It had incremental cost-effectiveness ratios (ICERs) of US$1,743 (95% CI: 1,673-1,799) per QALY, with a benefit cost ratio (BCR) of 1.98 (95% CI: 1.94–2.02), compared with routine strategy. While the LEN-LA strategy may be the most effective, its high cost, coupled with the highest ICER, currently presents a considerable obstacle to its widespread adoption. The ICERs were most affected by the probability of HIV infection, intervention cost and coverage. Conclusions SMS strategy for preventing HIV among MSM in China is cost-effective and could be a promising strategy for HIV prevention. These findings may have implications for public health policy and resource allocation in HIV prevention efforts targeting high-risk populations.

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance.

Background Allogeneic hematopoietic stem cell transplantation (HSCT) is essential for the long-term survival of acute leukemia (AL) patients. Peripheral blood stem cell transplantation has become the preferred method due to its lower invasiveness and convenience. The blood cell separator is key for hematopoietic progenitor cell collection, with CD34+ cell collection efficiency (CE) influencing transplant outcomes and healthcare costs. This study compares the cost-effectiveness of two devices, Spectra Optia and COM.TEC apheresis system, to assess their impact on CD34+ cell CE in HSCT. Methods A decision tree–Markov model was constructed from the perspective of the healthcare system to simulate the 5-year outcomes of 10,000 acute leukemia patients experiencing allogeneic stem cell transplantation. Resource cost differences between two devices were analyzed under two scenarios: minimizing resource consumption (scenario 1) and maximizing CD34+ cell volume (scenario 2). Cost-effectiveness analysis was undertaken combining quality-adjusted life years (QALYs) and total medical costs, with incremental net monetary benefit calculated at predefined willingness-to-pay thresholds. Results Compared to COM.TEC, Spectra Optia reduced apheresis times by 1–6 percent and improved the rate of engraftment success by 0.23 percent to 1.82 percent. In scenario 1, the net benefit per patient ranged from $634.85 to $1,260.43, and in scenario 2, the net benefit per patient ranged from $169.62 to $256.65. Conclusion Higher CE of the apheresis system optimizes both healthcare resource consumption and clinical outcomes. In the analysis considering the device price, Spectra Optia achieved cost-effectiveness dominance over COM.TEC at a surgical volume exceeding 121 cases per device.

Background: Tuberculosis continues to be a significant global burden. Purified protein derivative of tuberculin (TB-PPD) is one type of tuberculin skin test (TST) and is used commonly for the auxiliary diagnosis of tuberculosis. The recombinant Mycobacterium tuberculosis fusion protein (EC) test is a new test developed in China. Objective: Evaluate the long-term economic implications of using the EC test compared with the TB-PPD test to provide a reference for clinical decision-making. Methods: The target population was people at a high risk persons of being infected with Mycobacterium tuberculosis . The outcome indicator was quality-adjusted life years (QALY). A cost–utility analysis was used to evaluate the long-term economic implications of using the EC test compared with the TB-PPD test. We employed a decision tree–Markov model from the perspective of the whole society within 77 years. Results: Compared with the TB-PPD test, the EC test had a lower cost but higher QALY. The incremental cost–utility ratio was −119,800.7381 CNY/QALY. That is, for each additional QALY, the EC test could save 119,800.7381 CNY: the EC test was more economical than the TB-PPD test. Conclusion: Compared with the TB-PPD test, the EC test would be more economical in the long term for the diagnosis of M. tuberculosis infection according our study.

A Markov tree is a probabilistic graphical model for a random vector indexed by the nodes of an undirected tree encoding conditional independence relations between variables. One possible limit distribution of partial maxima of samples from such a Markov tree is a max-stable Hüsler–Reiss distribution whose parameter matrix inherits its structure from the tree, each edge contributing one free dependence parameter. Our central assumption is that, upon marginal standardization, the data-generating distribution is in the max-domain of attraction of the said Hüsler–Reiss distribution, an assumption much weaker than the one that data are generated according to a graphical model. Even if some of the variables are unobservable (latent), we show that the underlying model parameters are still identifiable if and only if every node corresponding to a latent variable has degree at least three. Three estimation procedures, based on the method of moments, maximum composite likelihood, and pairwise extremal coefficients, are proposed for usage on multivariate peaks over thresholds data when some variables are latent. A typical application is a river network in the form of a tree where, on some locations, no data are available. We illustrate the model and the identifiability criterion on a data set of high water levels on the Seine, France, with two latent variables. The structured Hüsler–Reiss distribution is found to fit the observed extremal dependence patterns well. The parameters being identifiable we are able to quantify tail dependence between locations for which there are no data.

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.

A new graphical model, called a vine, for dependent random variables is introduced. Vines generalize the Markov trees often used in modelling high-dimensional distributions. They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence. Vines can be used to specify multivariate distributions in a straightforward way by specifying various marginal distributions and the ways in which these marginals are to be coupled. Such distributions have applications in uncertainty analysis where the objective is to determine the sensitivity of a model output with respect to the uncertainty in unknown parameters. Expert information is frequently elicited to determine some quantitative characteristics of the distribution such as (rank) correlations. We show that it is simple to construct a minimum information vine distribution, given such expert information. Sampling from minimum information distributions with given marginals and (conditional) rank correlations specified on a vine can be performed almost as fast as independent sampling. A special case of the vine construction generalizes work of Joe and allows the construction of a multivariate normal distribution by specifying a set of partial correlations on which there are no restrictions except the obvious one that a correlation lies between - 1 and 1.

We consider general classes of gradient models on regular trees with spin values in a countable Abelian group S such as Z or Z q . This includes unbounded spin models like the p -SOS model and finite-alphabet clock models. Under a strong coupling (low temperature) condition on the interaction, we prove the existence of families of distinct homogeneous tree-indexed Markov chain Gibbs states μ A whose single-site marginals concentrate on a given finite subset A ⊂ S of spin values. The existence of such states is a new and robust phenomenon which is of particular relevance for infinite spin models. These states are extremal in the set of homogeneous Gibbs states, and in particular cannot be decomposed into homogeneous Markov-chain Gibbs states with a single-valued concentration center. Whether they are also extremal in the set of all Gibbs states remains an open, challenging question. As a further application of the method we obtain the existence of new types of gradient Gibbs states with Z -valued spins, whose single-site marginals do not localize, but whose correlation structure depends on the finite set A , where we provide explicit expressions for the correlation between the height-increments along disjoint edges.

There are three indispensable, yet contrasting requirements for a watermarking scheme: perceptual transparency, watermark capacity, and robustness against attacks. Therefore, a watermarking scheme should provide a trade-off among these requirements from the information-theoretic perspective. In this paper, we propose a statistical image watermark decoder based on the local frequency-domain Exponent-Fourier moments modeling, which can achieve the tradeoff among imperceptibility, robustness and data payload. The frequency-domain EFMs magnitudes are first generated by combining stationary wavelet transform (SWT) and Exponent-Fourier moments (EFMs). We divide the target region to select local frequency-domain EFMs magnitudes, which are planned to embed watermarks, statistical modeling, and extract watermarks. In order to achieve an accurate modeling process, we conduct the comprehensive statistical analyses of local frequency-domain EFMs magnitudes and establish the powerful Beta Generalized Weibull mixtures-based hidden Markov tree (BGW-HMT) model, which can take into account the non-Gaussian distribution characteristic and the interscale dependency at the same time. The Expectation/Conditional Maximisation Either (ECME) algorithm and upward–downward algorithm are successfully applied to estimate the parameters of BGW-HMT model. At the receiver, the BGW-HMT model is used in the design process of watermark decoder. The decoder structure is developed by using the maximum likelihood decision. In order to prove the effectiveness of proposed watermarking scheme, multi-angle performance tests are performed, including imperceptibility, robustness, watermark capacity and time complexity. The corresponding experimental results from the above four perspectives are inspiring. Compared with the state-of-the-art schemes, our statistical decoder is significantly superior to other statistical decoders.