Models for reasoning under uncertainty (abstract). - Richard E. Neapolitan (ACM Conference on Computer Science, 1986)

Models for reasoning under uncertainty. - Richard E. Neapolitan (Appl. Artif. Intell., 1987)

A Note of caution on Combining Certainties. - Richard E. Neapolitan (Int. J. Pattern Recognit. Artif. Intell., 1987)

Probabilistic reasoning in expert systems - theory and algorithms. - Richard E. Neapolitan (Wiley, 1990)

Investigation of Variances in Belief Networks. - Richard E. Neapolitan; James R. Kenevan (UAI, 1991)

An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks. - Peter Che; Richard E. Neapolitan; James R. Kenevan; Martha W. Evens (UAI, 1993)

The interpretation and application of belief functions. - Richard E. Neapolitan (Appl. Artif. Intell., 1993)

Is higher-order uncertainty needed? - Richard E. Neapolitan (IEEE Trans. Syst. Man Cybern. Part A, 1996)

Cognitive Processing of Causal Knowledge. - Scott B. Morris; Doug Cork; Richard E. Neapolitan (UAI, 1997)

Data Mining - Know It All. - Soumen Chakrabarti; Earl Cox; Eibe Frank; Ralf Hartmut Güting; Jiawei Han 001; Xia Jiang; Micheline Kamber; Sam Lightstone; Thomas P. Nadeau; Richard E. Neapolitan; Dorian Pyle; Mamdouh Refaat; et al. (Morgan Kaufmann, 2008)

A Polemic for Bayesian Statistics. - Richard E. Neapolitan (Innovations in Bayesian Networks, 2008)

Foundations of Algorithms (4. ed.). - Richard E. Neapolitan; Kumarss Naimipour (Jones and Bartlett, 2011)

Learning genetic epistasis using Bayesian network scoring criteria. - Xia Jiang; Richard E. Neapolitan; M. Michael Barmada; Shyam Visweswaran (BMC Bioinform., 2011)

Computation of Variances in Causal Networks - Richard E. Neapolitan; James R. Kenevan (CoRR, 2013)

Justifying the Principle of Interval Constraints - Richard E. Neapolitan; James R. Kenevan (CoRR, 2013)

A Note of Caution on Maximizing Entropy. - Richard E. Neapolitan; Xia Jiang (Entropy, 2014)

Utilizing Multidimensional Computer Adaptive Testing to Mitigate Burden With Patient Reported Outcomes. - Michael Bass; Scott B. Morris; Richard E. Neapolitan (AMIA, 2015)

Evaluation of a two-stage framework for prediction using big genomic data. - Xia Jiang; Richard E. Neapolitan (Briefings Bioinform., 2015)

Discovering causal interactions using Bayesian network scoring and information gain. - Zexian Zeng; Xia Jiang; Richard E. Neapolitan (BMC Bioinform., 2016)

Advancing the efficiency and efficacy of patient reported outcomes with multivariate computer adaptive testing. - Scott B. Morris; Mike Bass; Mirinae Lee; Richard E. Neapolitan (J. Am. Medical Informatics Assoc., 2017)

Using natural language processing and machine learning to identify breast cancer local recurrence. - Zexian Zeng; Sasa Espino; Ankita Roy; Xiaoyu Li 006; Seema A. Khan; Susan E. Clare; Xia Jiang; Richard E. Neapolitan; Yuan Luo 001 (BMC Bioinform., 2018)

Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis. - Xia Jiang; Alan Wells; Adam Brufsky; Darshan Shetty; Kahmil Shajihan; Richard E. Neapolitan (BMC Bioinform., 2020)