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13
Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data
, 2013
"... entropy ..."
Mind reading by machine learning: A doubly Bayesian method for inferring mental representations
- Proceedings of the 32nd Annual Conference of the Cognitive Science Society
, 2010
"... A central challenge in cognitive science is to measure and quantify the mental representations humans develop – in other words, to ‘read ’ subject’s minds. In order to eliminate potential biases in reporting mental contents due to verbal elaboration, subjects ’ responses in experiments are often lim ..."
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Cited by 6 (0 self)
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limited to binary decisions or discrete choices that do not require conscious reflection upon their mental contents. However, it is unclear what such impoverished data can tell us about the potential richness and dynamics of subjects ’ mental representations. To address this problem, we used ideal
Nested Sampling for Bayesian Model Comparison in the Context of Salmonella Disease Dynamics
, 2013
"... Understanding the mechanisms underlying the observed dynamics of complex biological systems requires the statistical assessment and comparison of multiple alternative models. Although this has traditionally been done using maximum likelihood-based methods such as Akaike’s Information Criterion (AIC) ..."
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Cited by 2 (0 self)
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), Bayesian methods have gained in popularity because they provide more informative output in the form of posterior probability distributions. However, comparison between multiple models in a Bayesian framework is made difficult by the computational cost of numerical integration over large parameter spaces. A
Model Selection for Likelihood-free Bayesian Methods Based on Moment Conditions: Theory and Numerical Examples. ArXiv e-prints
, 2014
"... An important practice in statistics is to use robust likelihood-free methods, such as the estimating equations, which only require assumptions on the moments instead of specifying the full probabilistic model. We propose a Bayesian flavored model selection approach for such likelihood-free methods, ..."
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, based on (quasi-)posterior probabilities from the Bayesian Generalized Method of Moments (BGMM). This novel concept allows us to incorporate two important advantages of a Bayesian approach: the expressiveness of posterior distributions and the convenient computational method of MCMC. Many dif
Gradient-based Stochastic Optimization Methods in Bayesian Experimental Design
, 2012
"... Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings. We pursue OED for nonlinear systems from a Bayesian perspective, with th ..."
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Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings. We pursue OED for nonlinear systems from a Bayesian perspective
Preliminary and incomplete
"... Over the past 20 years, automatic teller machines (ATMs) have become a ubiquitous component of consumer banking. Despite their vast presence, ATM prices have risen substantially over the last several years. The rise in prices together with the inherent geographic product differentiation for ATMs imp ..."
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entry and price in a simultaneous Bayesian-Nash equilibrium. We estimate the parameters of this model using data from the Minnesota-Iowa border. We develop a simulation-based likelihood estimator that is identified by the quasi-experimental variation created by the fact that Iowa fixed the price of ATMs
Internal External Examiner
"... ii Hierarchical spatial modelling is useful for modelling complex spatially correlated data in a variety of settings. Due to the complexity of spatial analyses, hierarchical spatial models for disease mapping studies have not generally found application at Vital Statistics agencies. Chapter 2 compar ..."
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compares penalized quasi-likelihood relative risk estimates to target values based on Bayesian Markov Chain Monte Carlo methods. Results show penalized quasi-likelihood to be a simple, reasonably accurate method of inference for exploratory studies of small-area relative risks and ranks of risks. Often
1Compressive Link Acquisition in Multiuser Communications
"... Abstract—An important receiver operation is to detect the presence specific preamble signals with unknown delays in the presence of scattering, Doppler effects and carrier offsets. This task, referred to as “link acquisition”, is typically a sequential search over the transmitted signal space. Recen ..."
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. Recently, many authors have suggested applying sparse recovery algorithms in the context of similar estimation or detection problems. These works typically focus on the benefits of sparse recovery, but not generally on the cost brought by compressive sensing. Thus, our goal is to examine the trade
Results 1 - 10
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