StatisticsStatisticshttp://hdl.handle.net/2262/762017-09-21T15:50:15Z2017-09-21T15:50:15ZBayesian nonparametric inference for discovery probabilities: credible intervals and large samples asymptoticsNIPOTI, BERNARDOhttp://hdl.handle.net/2262/818002017-09-21T10:17:22Z2017-01-01T00:00:00ZBayesian nonparametric inference for discovery probabilities: credible intervals and large samples asymptotics
NIPOTI, BERNARDO
PUBLISHED; DOI: http://dx.doi.org/10.5705/ss.202015.0250
2017-01-01T00:00:00ZJoint Audio Visual Retrieval for Tennis BroadcastsDAHYOT, ROZENNhttp://hdl.handle.net/2262/817652017-09-21T02:02:51Z2003-01-01T00:00:00ZJoint Audio Visual Retrieval for Tennis Broadcasts
DAHYOT, ROZENN
PUBLISHED; Hong Kong
2003-01-01T00:00:00ZBayesian inference for misaligned irregular time series with application to palaeoclimate reconstructionDoan, Thinh K.http://hdl.handle.net/2262/795732017-07-11T02:02:01Z2015-01-01T00:00:00ZBayesian inference for misaligned irregular time series with application to palaeoclimate reconstruction
Doan, Thinh K.
This thesis proposes new Bayesian methods to jointly analyse misaligned irregular time series. Temporal misalignment occurs wdien multiple irregularly spaced time series are considered together, or when the time periods defining the data points are not the same across different series. Other issues under consideration include errors in the time scales, and non-Gaussian processes for underlying latent values. Our proposed models are hierarchical.
2015-01-01T00:00:00ZVariational Bayes approximation for inverse regression problemsVatsa, Richahttp://hdl.handle.net/2262/793832017-02-10T11:43:36Z2011-01-01T00:00:00ZVariational Bayes approximation for inverse regression problems
Vatsa, Richa
Inverse regression is a tool to predict an unknown explanatory variable for given observations of a response variable in a regression problem. The prediction problem is usually carried out in two stages: firstly, to fit the model relationship between the variables, and secondly, to predict the unknown explanatory variable. Both the problems, model fitting and prediction involve considerable computational burden. Previous work on the Bayesian approach to the problem have used MCMC, INLA and other numerical methods. This thesis aims to present an alternative fast variational Bayes (VB) approximation to Bayesian inference for inverse regression problems which claims to avoid the limitations of previous work. The VB method assumes independence between the parameters in the posterior distribution, thus provides fast approximations to Bayesian estimation problems. In contrast to INLA, it can be applied to models with many unknown parameters. In the thesis, the VB method is applied to a wider class of inverse regression problems classified into two classes: inverse latent regression and inverse non-latent regression which present challenges for the methodâ€™s accuracy and tractability. The VB method itself is not without limitations. Quick VB solutions are obtained at the cost of some loss of accuracy. Also, tractable application of the method is limited to conjugate- exponential (CE) models. It is attempted to increase the accuracy and tractability of the method outside CE models with the use of further approximations, such as a Gaussian approximation.
2011-01-01T00:00:00Z