Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic similarity networks
Probabilistic similarity networks
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
A linear constraint satisfaction approach to cost-based abduction
Artificial Intelligence
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
On Spline Approximations for Bayesian Computations
On Spline Approximations for Bayesian Computations
| Hi-index | 0.00 |
Probabilistic reasoning typically suffers from the explosive amount of information it must maintain. There are a variety of methods available for curbing this explosion. However, in doing so, it is important to avoid oversimplifying the given domain through injudicious use of assumptions such as independence. Multiple splining is an approach for compressing and approximating the probabilistic information. Instead of positing additional independence conditions, it attempts to identify patterns in the information. While the data explosion is multiplicative in nature, O(n_1n_2{\cdots}n_k), multiple splines reduces it to an additive one, O(n_1 + n_2 + \cdots + n_k). We consider how these splines can be found and used. Since splines exploit patterns in the data, we can also use them to help in filling in missing data. As it turns out, our splining method is quite general and may be applied to other domains besides probabilistic reasoning which can benefit from data compression.