  In this work, we explore the application of likelihood ratio as a forensic
evidence assessment tool to evaluate the causal mechanism of a bloodstain
pattern. It is assumed that there are two competing hypotheses regarding the
cause of a bloodstain pattern. The bloodstain patterns are represented as a
collection of ellipses with each ellipses characterized by its location, size
and orientation. Quantitative measures and features are derived to summarize
key aspects of the patterns. A bivariate Gaussian model is chosen to estimate
the distribution of features under a given hypothesis and thus approximate the
likelihood of a pattern. Published data with 59 impact patterns and 55 gunshot
patterns is used to train and evaluate the model. Results demonstrate the
feasibility of the likelihood ratio approach for bloodstain pattern analysis.
The results also hint at some of the challenges that need to be addressed for
future use of the likelihood ratio approach for bloodstain pattern analysis.

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