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Multi-nuclear, multi-dimensional Fourier transform (FT) NMR spectroscopy has ushered in a broad range of new applications in chemistry and biology. Multi-dimensional NMR experimental data comes at a price, however. Additional dimensions lead to exponential increases in data collection time, and the number of data points required increases at polynomial rates with the spectrometer field strength. Reducing data collection times while retaining the benefits of multi-dimensional experiments is one of the major challenges in NMR spectroscopy. This chapter focuses on the use of methods collectively referred to as reduced dimensionality experiments (RD) and their application to the recovery of spectral parameters—for example, peak positions. While standard sampling approaches can interpret the data using the standard and established computational methods rooted in the Fourier transform theory, RD methods can benefit from alternative approaches that are coupled to the ideas of RD. In this context, the application and the utility of statistical and adaptive Bayesian approaches are discussed.

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