title: Māori Goddesses in Literature Part 1: pre 1880 - 1900

authors: Yates-Smith, Aroha

subjects: Atua wāhine

abstract: Many contemporary Māori are familiar with atua wāhine, Māori goddesses, through accounts that have been passed on to them by word of mouth. Few, however, have had an opportunity to consult accounts of atua wāhine that date from the early period of Māori-Pākehā contact. Those who wish to do so may have considerable difficulty in sifting through all of the written materials for the information they seek. For this reason, this paper provides a review of the literature pertaining to atua wāhine from pre-1880 to 1900, together with a commentary on the sources to which reference is made. A review of literature on atua wāhine which appeared after 1900 will appear in the second volume of this journal.

text: Māori Goddesses in Literature Part 1: pre 1880 - 1900 Many contemporary Māori are familiar with atua wāhine, Māori goddesses, through accounts that have been passed on to them by word of mouth. Few, however, have had an opportunity to consult accounts of atua wāhine that date from the early period of Māori-Pākehā contact. Those who wish to do so may have considerable difficulty in sifting through all of the written materials for the information they seek. For this reason, this paper provides a review of the literature pertaining to atua wāhine from pre-1880 to 1900, together with a commentary on the sources to which reference is made. A review of literature on atua wāhine which appeared after 1900 will appear in the second volume of this journal.

year: 2000-10

title: Review: Memoirs of Active Service

authors: Cheesman, Sue

subjects: Maaka Pepene

abstract: Ka maumahara tonu tatou ki a ratou - we will remember them…poignant last words…   bidding farewell to a generation of veterans moving on...leaving fragments  of memories and diaries for children/grandchildren to honour, celebrate and remember …black costumes billow, fold, swirl and wrap the dancers bodies…wide sweeping arm movements…jumps and turns…slides on all fours - provides the vehicle for five accomplished dancers to fill the space with fluidity and lyricism in this very moving finale, a lament set to Albinoni’s adagio - maybe old soldiers never die.

text: Review: Memoirs of Active Service Ka maumahara tonu tatou ki a ratou - we will remember them…poignant last words…   bidding farewell to a generation of veterans moving on...leaving fragments  of memories and diaries for children/grandchildren to honour, celebrate and remember …black costumes billow, fold, swirl and wrap the dancers bodies…wide sweeping arm movements…jumps and turns…slides on all fours - provides the vehicle for five accomplished dancers to fill the space with fluidity and lyricism in this very moving finale, a lament set to Albinoni’s adagio - maybe old soldiers never die.

year: 2007

title: Conditional density estimation with class probability estimators

authors: Frank, Eibe

subjects: computer science

abstract: Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it. In this paper we compare three techniques for computing conditional density estimates using a class probability estimator, where this estimator is applied to the discretized target variable and used to derive instance weights for an underlying univariate density estimator; this yields a conditional density estimate. The three density estimators we compare are: a histogram estimator that has been used previously in this context, a normal density estimator, and a kernel estimator. In our experiments, the latter two deliver better performance, both in terms of cross-validated log-likelihood and in terms of quality of the resulting prediction intervals. The empirical coverage of the intervals is close to the desired confidence level in most cases. We also include results for point estimation, as well as a comparison to Gaussian process regression and nonparametric quantile estimation.

text: Conditional density estimation with class probability estimators Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it. In this paper we compare three techniques for computing conditional density estimates using a class probability estimator, where this estimator is applied to the discretized target variable and used to derive instance weights for an underlying univariate density estimator; this yields a conditional density estimate. The three density estimators we compare are: a histogram estimator that has been used previously in this context, a normal density estimator, and a kernel estimator. In our experiments, the latter two deliver better performance, both in terms of cross-validated log-likelihood and in terms of quality of the resulting prediction intervals. The empirical coverage of the intervals is close to the desired confidence level in most cases. We also include results for point estimation, as well as a comparison to Gaussian process regression and nonparametric quantile estimation.

year: 2009