122,031 research outputs found
Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC.
No full textWe consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data
Antibiotic treatment and appendectomy for uncomplicated acute appendicitis in adults and children: A systematic review and meta-analysis
No full textObjective: The aim of this meta-analysis was to summarize the current available evidence on nonoperative management (NOM) with antibiotics for uncomplicated appendicitis, both in adults and children.Summary Background Data: Although earlier meta-analyses demonstrated that NOM with antibiotics may be an acceptable treatment strategy for patients with uncomplicated appendicitis, evidence is limited by conflicting results.Methods: Systematic literature search was performed using MEDLINE, the Cochrane Central Register of Controlled Trials, and EMBASE databases for randomized and nonrandomized studies comparing antibiotic therapy (AT) and surgical therapy-appendectomy (ST) for uncomplicated appendicitis. Literature search was completed in August 2018.Results: Twenty studies comparing AT and ST qualified for inclusion in the quantitative synthesis. In total, 3618 patients were allocated to AT (n = 1743) or ST (n = 1875). Higher complication-free treatment success rate (82.3% vs 67.2%; P < 0.00001) and treatment efficacy based on 1-year follow-up rate (93.1% vs 72.6%; P < 0.00001) were reported for ST. Index admission antibiotic treatment failure and rate of recurrence at 1-year follow-up were reported in 8.5% and 19.2% of patients treated with antibiotics, respectively. Rates of complicated appendicitis with peritonitis identified at the time of surgical operation (AT: 21.7% vs ST: 12.8%; P = 0.07) and surgical complications (AT: 12.8% vs ST: 13.6%; P = 0.66) were equivalent.Conclusions: Antibiotic therapy could represent a feasible treatment option for image-proven uncomplicated appendicitis, although complication-free treatment success rates are higher with ST. There is also evidence that NOM for uncomplicated appendicitis does not statistically increase the perforation rate in adult and pediatric patients receiving antibiotic treatment. NOM with antibiotics may fail during the primary hospitalization in about 8% of cases, and an additional 20% of patients might need a second hospitalization for recurrent appendicitis
Exact Bayesian curve fitting and signal segmentation.
No full textWe consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem
A sequential smoothing algorithm with linear computational cost.
No full textIn this paper we propose a new particle smoother that has a computational complexity of O(N), where N is the number of particles. This compares favourably with the O(N2) computational cost of most smoothers. The new method also overcomes some degeneracy problems in existing algorithms. Through simulation studies we show that substantial gains in efficiency are obtained for practical amounts of computational cost. It is shown both through these simulation studies, and by the analysis of an athletics dataset, that our new method also substantially outperforms the simple filter-smoother, the only other smoother with computational cost that is O(N)
Perfect Simulation From Nonneutral Population Genetic Models: Variable Population Size and Population Subdivision.
No full textWe show how the idea of monotone coupling from the past can produce simple algorithms for simulating samples at a nonneutral locus under a range of demographic models. We specifically consider a biallelic locus and either a general variable population size mode or a general migration model for population subdivision. We investigate the effect of demography on the efficacy of selection and the effect of selection on genetic divergence between populations
Exact and efficient Bayesian inference for multiple changepoint problems.
No full textWe demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on coal-mining disaster data and on well-log data. Our method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes
On the Choice of Genetic Distance in Spatial-Genetic Studies.
No full textWe look at how to choose genetic distance so as to maximise the power of detecting spatial structure. We answer this question through analysin g two population genetic models that allow for a spatially structured population in a continuous habitat. These models, like most that incorporate spatial s tructure, can be characterised by a separation of time scales: the history of the sample can be split into a scattering and collecting phase, and it is only during the scattering phase that the spatial locations of the sample affects the coalescence times. Our results suggest that the optimal choice of genetic distance is based upon splitting a DNA sequence into segments, and counting the number of segments at which two sequences differ. The size of these segments depends on the length of the scattering phase for the population genetic model
Online Inference for Multiple Changepoint Problems.
No full textWe propose an on-line algorithm for exact filtering of multiple changepoint problems. This algorithm enables simulation from the true joint posterior distribution of the number and position of the changepoints for a class of changepoint models. The computational cost of this exact algorithm is quadratic in the number of observations. We further show how resampling ideas from particle filters can be used to reduce the computational cost to linear in the number of observations, at the expense of introducing small errors; and propose two new, optimum resampling algorithms for this problem. One, a version of rejection control, allows the particle filter to automatically choose the number of particles required at each time-step. The new resampling algorithms substantially out-perform standard resampling algorithms on examples we consider; and we demonstrate how the resulting particle filter is practicable for segmentation of human GC content
sequenceLDhot: Detecting Recombination Hotspots.
No full textMotivation: There is much local variation in recombination rates across the human genome—with the majority of recombination occuring in recombination hotspots—short regions of around ~2 kb in length that have much higher recombination rates than neighbouring regions. Knowledge of this local variation is important, e.g. in the design and analysis of association studies for disease genes. Population genetic data, such as that generated by the HapMap project, can be used to infer the location of these hotspots. We present a new, efficient and powerful method for detecting recombination hotspots from population data. Results: We compare our method with four current methods for detecting hotspots. It is orders of magnitude quicker, and has greater power, than two related approaches. It appears to be more powerful than HotspotFisher, though less accurate at inferring the precise positions of the hotspot. It was also more powerful than LDhot in some situations: particularly for weaker hotspots (10–40 times the background rate) when SNP density is lower (< 1/kb). Availability: Program, data sets, and full details of results are available at: http://www.maths.lancs.ac.uk/~fearnhea/Hotspot
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