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This paper extends previous research on using a Bayesian network model to investigate impacts of time (month) and weather (number of fair weather days in a week) on animal-related outages in distribution systems. Outage history (outages in the previous week) is included as an additional input to the model, and inputs and outputs are classified systematically to reduce errors in estimates of outputs. Conditional probability table obtained from the historical data are used to estimate weekly animal-related outages which is followed by a Monte Carlo simulation to find estimates of mean and confidence limits for monthly animal-related outages. Comparison of results obtained for four cities of different sizes in Kansas with those obtained using a hybrid wavelet/neural network model shows consistency between the two models. The methodology presented in this paper is simple to implement and useful for the utilities for year-end analysis of the outage data to identify specific reliability-related concerns. [ABSTRACT FROM PUBLISHER]