Many authors make distinctions between clinical trial designs derived using “group sequential designs” (e.g., O’Brien-Fleming or Pocock designs), “error spending functions” (e.g., Lan & DeMets or Hwang, Shih, & DeCani), “stochastic curtailment” (e.g., conditional power or Bayesian predictive power), and “Bayesian designs” based on posterior probabilities. However, in RCTdesign we regard these a...

Word sequential alignment models work well for similar language pairs, but they are quite inadequate for distant language pairs. It is difficult to align words or phrases of distant languages with high accuracy without structural information of the sentences. In this paper, we propose a Bayesian subtree alignment model that incorporates dependency relations between subtrees in dependency tree s...

Posterior Cramér Rao lower bounds (PCRLBs) [1] for sequential Bayesian estimators provide performance bounds for general nonlinear filtering problems and have been used widely for sensor management in tracking and fusion systems. However, the unconditional PCRLB [1] is an off-line bound that is obtained by taking the expectation of the Fisher information matrix (FIM) with respect to the measure...

Sequential Monte Carlo is a family of algorithms for sampling from a sequence of distributions. Some of these algorithms, such as particle filters, are widely used in the physics and signal processing researches. More recent developments have established their application in more general inference problems such as Bayesian modeling. These algorithms have attracted considerable attentions in rec...

Sequential filtering provides an optimal framework for estimating and updating the unknown parameters of a system as data become available. Despite significant progress in the general theory and implementation, sequential Bayesian filters have been sparsely applied to ocean acoustics. The foundations of sequential Bayesian filtering with emphasis on practical issues are first presented covering...

Sequential Monte Carlo (SMC) methods are a class of importance sampling and resampling techniques designed to simulate from a sequence of probability distributions. These approaches have become very popular over the last few years to solve sequential Bayesian inference problems (e.g. Doucet et al. 2001). However, in comparison to Markov chain Monte Carlo (MCMC), the application of SMC remains l...

Determine a non-myopic solution to the sequential decision making problem of monitoring and optimising a space and time dependent function using a moving sensor. Contributions: Sequential Bayesian Optimisation (SBO) Formulate SBO as a Partially Observed Markov Decision Process (POMDP). Find non-mypic solution for the POMDP analog of SBO using MonteCarlo Tree Search (MCTS) and Upper Confidence B...

Sequential Bayesian filters, such as particle filters, are often presented as an ideal means of tracking the state of nonlinear systems. Here shadowing filters are demonstrated to perform better than sequential filters at tracking under specific circumstances. The success of shadowing filters is attributed to avoiding both well-known deficiencies of particle filters, and some newly identified p...

Abstract Running developmental experiments, particularly with infants, is often time‐consuming and intensive, the recruitment of participants hard expensive. Thus, an important goal for researchers to optimize sampling plans such that neither too many nor few are tested given hypothesis interest. One approach enables optimization use Bayesian sequential designs. The designs allows data collecti...

We present a fast Bayesian algorithm for the segmentation ofremote-sensing images. It alternates two processing steps, the binary Bayesian segmentation of regions, and the separation of non-connected same-class regions, which both present relatively low complexity. As a result, a detailed and reliable K-region segmentation map can be obtained in limited CPU-time. In addition, the map is organiz...