257219
On the Revision of Probabilistic Beliefs using Uncertain Evidence
We revisit the problem of revising probabilistic beliefs using uncertain evidence, and report results on several major issues relating to this problem: how should one specify uncertain evidence? How should one revise a probability distribution? How should one interpret informal evidential statements? Should, and do, iterated belief revisions commute? And what guarantees can be offered on the amount of belief change induced by a particular revision? Our discussion is focused on two main methods for probabilistic revision: Jeffrey's rule of probability kinematics and Pearl's method of virtual evidence, where we analyze and unify these methods from the perspective of the questions posed above.
5935513
On Bayesian Network Approximation by Edge Deletion
We consider the problem of deleting edges from a Bayesian network for the purpose of simplifying models in probabilistic inference. In particular, we propose a new method for deleting network edges, which is based on the evidence at hand. We provide some interesting bounds on the KL-divergence between original and approximate networks, which highlight the impact of given evidence on the quality of approximation and shed some light on good and bad candidates for edge deletion. We finally demonstrate empirically the promise of the proposed edge deletion technique as a basis for approximate inference.
1880699
A distance measure for bounding probabilistic belief change
We propose a distance measure between two probability distributions, which allows one to bound the amount of belief change that occurs when moving from one distribution to another. We contrast the proposed measure with some well known measures, including KL-divergence, showing how they fail to be the basis for bounding belief change as is done using the proposed measure. We then present two practical applications of the proposed distance measure: sensitivity analysis in belief networks and probabilistic belief revision. We show how the distance measure can be easily computed in these applications, and then use it to bound global belief changes that result from either the perturbation of local conditional beliefs or the accommodation of soft evidence. Finally, we show that two well known techniques in sensitivity analysis and belief revision correspond to the minimization of our proposed distance measure and, hence, can be shown to be optimal from that viewpoint.
10310314
Sensitivity Analysis in Markov Networks
This paper explores the topic of sensitivity analysis in Markov networks, by tackling questions similar to those arising in the context of Bayesian networks: the tuning of parameters to satisfy query constraints, and the bounding of query changes when perturbing network parameters. Even though the distribution induced by a Markov network corresponds to ratios of multi-linear functions, whereas the distribution induced by a Bayesian network corresponds to multi-linear functions, the results we obtain for Markov networks are as effective computationally as those obtained for Bayesian networks. This similarity is due to the fact that conditional probabilities have the same functional form in both Bayesian and Markov networks, which turns out to be the more influential factor. The major difference we found, however, is in how changes in parameter values should be quantified, as such parameters are interpreted differently in Bayesian networks and Markov networks.
3053066
Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes, and to identify single parameter changes that would enforce a certain query constraint. In this paper, we expand the work to multiple parameters which may be in the CPT of a single variable, or the CPTs of multiple variables. Not only do we identify the solution space of multiple parameter changes that would be needed to enforce a query constraint, but we also show how to find the optimal solution, that is, the one which disturbs the current probability distribution the least (with respect to a specific measure of disturbance). We characterize the computational complexity of our new techniques and discuss their applications to developing and debugging Bayesian networks, and to the problem of reasoning about the value (reliability) of new information.
17538498
Reasoning about Bayesian Network Classifiers
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDS), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for converting any naive Bayes classifier into an ODD, and we show theoretically and experimentally that this algorithm can give us an ODD that is tractable in size cvcn given an intractable number of instances. Since ODDS are tractable representations of classifiers, our algorithm allows us to efficiently test the equivalence of two naive Bayes classifiers and characterize discrepancies between them. We also show a number of additional results including a count of distinct classifiers that can be induced by changing some CPT in a naive Bayes classifier, and the range of allowable changes to a CPT which keeps the current classifier unchanged.
6130133
Security and Privacy in Sensor Networks
Sensor networks offer economically viable solutions for a variety of applications. For example, current implementations monitor factory instrumentation, pollution levels, freeway traffic, and the structural integrity of buildings. Other applications include climate sensing and control in office buildings and home environmental sensing systems for temperature, light, moisture, and motion. Sensor networks are key to the creation of smart spaces, which embed information technology in everyday home and work environments. The miniature wireless sensor nodes, or motes, developed from low-cost off-the-shelf components at the University of California, Berkeley, as part of its smart dust projects, establish a self-organizing sensor network when dispersed into an environment. The privacy and security issues posed by sensor networks represent a rich field of research problems. Improving network hardware and software may address many of the issues, but others will require new supporting technologies.
5337517
Throughput Optimized SHA-1 Architecture Using Unfolding Transformation
In this paper, the authors analyze the theoretical delay bound of the SHA-1 algorithm and propose architectures to achieve high throughput hardware implementations which approach this bound. According to the results of FPGA implementations, 3,541 Mbps with a pipeline and 893 Mbps without a pipeline were achieved. Moreover, synthesis results using 0.18mum CMOS technology showed that 10.4 Gbps with a pipeline and 3.1 Gbps without a pipeline can be achieved. These results are much faster than previously published results. The high throughputs are due to the unfolding transformation, which reduces the number of required cycles for one block hash. The authors reduced the required number of cycles to 12 cycles for a 512 bit block and showed that 12 cycles is the optimal in our design
2499858
Location updates and probabilistic tracking algorithms for mobile cellular networks
In this paper, we develop a novel mathematical model to analyze different location update protocols for mobile cellular network. Our model can capture many important features of user movements, such as geographically dependent movement, directional movement and non-exponential distribution of cell dwell times. We propose to use the transient method to predict the location of a mobile user. This method is efficient and, more importantly, able to address the temporal change in the current location of a mobile user. Therefore, the accuracy of the prediction of the location of a user can be enhanced. We also propose a new location update protocol which can attain a lower trading cost and also adapt to different mobility patterns. Transient performance analysis is also carried out and the results are compared with other protocols.
27492259
Generalized Filtered Back-Projection Reconstruction in Breast Tomosynthesis
8514683
Energy efficient online deadline scheduling
This paper extends the study of online algorithms for energy-efficient deadline scheduling to the overloaded setting. Specifically, we consider a processor that can vary its speed between 0 and a maximum speed T to minimize its energy usage (of which the rate is roughly a cubic function of the speed). As the speed is upper bounded, the system may be overloaded with jobs and no scheduling algorithms can meet the deadlines of all jobs. An optimal schedule is expected to maximize the throughput, and furthermore, its energy usage should be the smallest among all schedules that achieve the maximum throughput. In designing a scheduling algorithm, one has to face the dilemma of selecting more jobs and being conservative in energy usage. Even if we ignore energy usage, the best possible online algorithm is 4-competitive on throughput [12]. On the other hand, existing work on energy-efficient scheduling focuses on minimizing the energy to complete all jobs on a processor with unbounded speed, giving several O(1)-competitive algorithms with respect to the energy usage [2, 20]. This paper presents the first online algorithm for the more realistic setting where processor speed is bounded and the system may be overloaded; the algorithm is O(1)-competitive on both throughput and energy usage. If the maximum speed of the online scheduler is relaxed slightly to (1 + ε)T for some ε > 0, we can improve the competitive ratio on throughput to arbitrarily close to one, while maintaining O(1)-competitive on energy usage.