1,453 research outputs found
Approximate Decentralized Bayesian Inference
This paper presents an approximate method for performing Bayesian inference
in models with conditional independence over a decentralized network of
learning agents. The method first employs variational inference on each
individual learning agent to generate a local approximate posterior, the agents
transmit their local posteriors to other agents in the network, and finally
each agent combines its set of received local posteriors. The key insight in
this work is that, for many Bayesian models, approximate inference schemes
destroy symmetry and dependencies in the model that are crucial to the correct
application of Bayes' rule when combining the local posteriors. The proposed
method addresses this issue by including an additional optimization step in the
combination procedure that accounts for these broken dependencies. Experiments
on synthetic and real data demonstrate that the decentralized method provides
advantages in computational performance and predictive test likelihood over
previous batch and distributed methods.Comment: This paper was presented at UAI 2014. Please use the following BibTeX
citation: @inproceedings{Campbell14_UAI, Author = {Trevor Campbell and
Jonathan P. How}, Title = {Approximate Decentralized Bayesian Inference},
Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}
No. 16: The State of Food Insecurity in Msunduzi Municipality, South Africa
There is plenty of food in Msunduzi, in South Africa’s KwaZulu-Natal province, but the urban poor regularly go hungry. This study of Msunduzi’s food security situation formed part of AFSUN’s baseline survey of eleven Southern African cities. The survey results show that the urban poor in Msunduzi are significantly worse off than their counterparts in Cape Town and Johannesburg. A third of the households reported that they sometimes or often have no food to eat of any kind. Household size did not make a great deal of difference to levels of insecurity but female-headed households are more food insecure than male-headed households. Msunduzi is a classic case study of a city whose food supply system is dominated by modern supermarket supply chains. The informal food economy is relatively small, urban agriculture is not especially significant in the city and informal rural-urban food transfers are lower than in many other cities surveyed. In this respect, Msunduzi offers the other cities a picture of their own future. Supermarket expansion is occurring at an extremely rapid rate throughout southern Africa, tying urban spaces and populations into global, regional and national supply chains. While supermarkets offer greater variety and fresher produce than many other outlets, they clearly do not meet the needs of the poor
Truncated Random Measures
Completely random measures (CRMs) and their normalizations are a rich source
of Bayesian nonparametric priors. Examples include the beta, gamma, and
Dirichlet processes. In this paper we detail two major classes of sequential
CRM representations---series representations and superposition
representations---within which we organize both novel and existing sequential
representations that can be used for simulation and posterior inference. These
two classes and their constituent representations subsume existing ones that
have previously been developed in an ad hoc manner for specific processes.
Since a complete infinite-dimensional CRM cannot be used explicitly for
computation, sequential representations are often truncated for tractability.
We provide truncation error analyses for each type of sequential
representation, as well as their normalized versions, thereby generalizing and
improving upon existing truncation error bounds in the literature. We analyze
the computational complexity of the sequential representations, which in
conjunction with our error bounds allows us to directly compare representations
and discuss their relative efficiency. We include numerous applications of our
theoretical results to commonly-used (normalized) CRMs, demonstrating that our
results enable a straightforward representation and analysis of CRMs that has
not previously been available in a Bayesian nonparametric context.Comment: To appear in Bernoulli; 58 pages, 3 figure
Ariadne: An interface to support collaborative database browsing:Technical Report CSEG/3/1995
This paper outlines issues in the learning of information searching skills. We report on our observations of the learning of browsing skills and the subsequent iterative development and testing of the Ariadne system – intended to investigate and support the collaborative learning of search skills. A key part of this support is a mechanism for recording an interaction history and providing students with a visualisation of that history that they can reflect and comment upon
Forward stagewise regression and the monotone lasso
We consider the least angle regression and forward stagewise algorithms for
solving penalized least squares regression problems. In Efron, Hastie,
Johnstone & Tibshirani (2004) it is proved that the least angle regression
algorithm, with a small modification, solves the lasso regression problem. Here
we give an analogous result for incremental forward stagewise regression,
showing that it solves a version of the lasso problem that enforces
monotonicity. One consequence of this is as follows: while lasso makes optimal
progress in terms of reducing the residual sum-of-squares per unit increase in
-norm of the coefficient , forward stage-wise is optimal per unit
arc-length traveled along the coefficient path. We also study a condition
under which the coefficient paths of the lasso are monotone, and hence the
different algorithms coincide. Finally, we compare the lasso and forward
stagewise procedures in a simulation study involving a large number of
correlated predictors.Comment: Published at http://dx.doi.org/10.1214/07-EJS004 in the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Virtue Theoretic Solution to the Problem of Moral Luck
At the beginning of his famous paper “Moral Luck,” Thomas Nagel notes that it is intuitively plausible that people cannot be morally assessed for what is beyond their control. He then argues that most, if not all, of what people do is beyond their control. Thus, Nagel concludes that individuals must deny that people cannot be morally assessed for what is beyond their control, alter the way they think about morality, or abandon the belief that moral assessment is possible. I contend that one’s best option is to alter the way one thinks about morality and therefore draw from the work of Michael J. Zimmerman to construct and defend a counterfactual theory of moral assessment which looks not only at the kind of person one is and the kinds of actions one performs but also at the kind of person one would be and the kinds of actions one would perform in certain counterfactual circumstances. In closing, I explain why one who accepts my counterfactual theory of moral assessment has reason to prefer virtue ethical theories of morality to their consequentialist and deontological counterparts
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