The simple slice sampler is a specialised type of MCMC auxiliary variable method (Swendsen and Wang, 1987; Edwards and Sokal, 1988; Besag and Green, 1993; Higdon, 1998) that has been popularised by Neal (1997), Fishman (1996), and Damien, Wakefield, and Walker (1999).
Sample text:```The methods working directly on the count data can be broadly divided into parametric (baySeq [14], EBSeq [24], ShrinkSeq [27], edgeR [23], DESeq [7], NBPSeq [15] and TSPM [13]) and non-parametric methods (NOISeq [25] and SAMseq [26]).```
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Sample text:```This transition was introduced first by Prince and Elder (2007) and it simplifies formulas for parameter update on Mstep of EM algorithm.```
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Sample text:```This section contains a generalisation of the basic results on multifractal products of stochastic processes developed in Kahane (1985), Kahane (1987) and Mannersalo et al (2002).```
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Sample text:```Participants were undergraduates who had been followed during their college career as part of a longitudinal cohort study (see Bonanno et al., 2004; Papa & Bonanno, 2008).```
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Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
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Sample text:```These models are not specified and observe a single phenomenon from different points of view (Maxfield et al., 2002; Parker et al., 2009).```
Sample intent: method
Sample text:```These models are not specified and observe a single phenomenon from different points of view (Maxfield et al., 2002; Parker et al., 2009).```
Sample intent: method



Sample text:```This section contains a generalisation of the basic results on multifractal products of stochastic processes developed in Kahane (1985), Kahane (1987) and Mannersalo et al (2002).```
Sample intent: method



Sample text:```This transition was introduced first by Prince and Elder (2007) and it simplifies formulas for parameter update on Mstep of EM algorithm.```
Sample intent: background



Sample text:```This seems to be the main drawback of the current implementation in comparison to the method of Black Jepson (“refined” in Table 1).```
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Sample text:```13 indicates the comparison of the results of the mesh simplification method [2] after applying different mesh enhancement techniques.```
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Sample text:```Following the experimental design proposed in Yannakakis and Hallam (2007c) and Yannakakis et al. (2008) for effectively capturing the level of entertainment, the test-bed game under investigation is played in variants.```
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Sample text:```The methods working directly on the count data can be broadly divided into parametric (baySeq [14], EBSeq [24], ShrinkSeq [27], edgeR [23], DESeq [7], NBPSeq [15] and TSPM [13]) and non-parametric methods (NOISeq [25] and SAMseq [26]).```
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Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
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Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method



Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
Sample intent: method