Weberian Model of Social Stratification: A Viewpoint

Occasional Papers in Sociology and Anthropology - Volume 7, 200

Microlensing Events of the LMC are Better Explained by Stars within the LMC Than by Machos

The recently reported microlensing events of the LMC have caused much excitement, and have been interpreted as due to `dark objects' (MACHOs) in the halo of our Galaxy. It is shown here that the stars within the LMC play a dominant role as gravitational lenses and can indeed account for the observed events. The MACHO event and one of the EROS events lie within the bar for which the probability of microlensing is consistent with being caused by an object within the LMC. If the stars within the LMC play a dominant role as lenses, the events should be concentrated towards the center of the LMC. If MACHOs play a dominant role as lenses then, for a given number of monitored stars, the events should be uniformly distributed over the whole of the LMC. Thus the galactic and the LMC lenses can be statistically distinguished in most cases. It is further shown that, under certain conditions, the light curve of an event caused by a star within the LMC would be different from the one caused by a MACHO. This can also be a distinguishing signature, and seems to have been observed in case of the MACHO event. The fit discrepancy near the peak which the authors say ``is not yet understood" would be a natural consequence if the event is caused by an object within the LMC, which further indicates that the lensing is due to a low mass star within the LMC itself.Comment: 14 pages, To appear in Sept., 1994 issue of Pub. Astron. Soc. Pac., Figures available on request from [email protected]

Community Development as a Strategy to Rural Development

Occasional Papers in Sociology and Anthropology - Volume 3, 199

Causal Inference by Stochastic Complexity

The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes