The consequences of Maternal Mortality (MM) cannot be overemphasized. It inhibits population growth resulting into loss of lives among others. This work tends to obtain the maternal mortality rates (MMR) in Nigeria, identify some fitted distributions to MMR and determine which of the distributions best fits the data. A comprehensive Exploratory Data Analysis (EDA) was carried on MM and the MMRs were obtained. Two parameters Gamma distribution, Weibull and Exponential distributions were fitted for MM. Both BIC and AIC selection criteria were adopted in selecting the most fitted distribution. The AIC for Gamma, Weibull and Exponential distributions fitted for MMR were 1339. 396, 1340. 161 and 370. 5244 respectively. Also, the BIC for Gamma, Weibull and Exponential distributions fitted for MM were 1344. 971, 1345. 736 and 373. 3119 respectively. It can be observed that Exponential distribution has the least AIC (370. 5244) and least BIC (373. 3119), therefore, it is the most fitted distribution for MM. The estimate (standard error) of exponential distribution on MM is 0. 5853 indicating the fitness of the distribution being the one with the least error. Conclusively, the model obtained from this study can be used to study and monitor MM in Nigeria to achieve a better economy and thus brings about local and national development.

We propose a dynamic program to find the shortest path in a network having gamma probability distributions as arc lengths. Two operators of sum and comparison need to be adapted for the proposed dynamic program. Convolution approach is used to sum two gamma probability distributions being employed in the dynamic program.

Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. The quality of wind speed assessment depends on the capability of chosen probability density function (PDF) to describe the measured wind speed frequency distribution. The objective of this study is to describe (model) wind speed characteristics using three mixture probability density functions Weibull-extreme value distribution (GEV), Weibull-lognormal, and GEV-lognormal which were not tried before. Statistical parameters such as maximum error in the Kolmogorov-Smirnov test, root mean square error, Chi-square error, coefficient of determination, and power density error are considered as judgment criteria to assess the fitness of the probability density functions. Results indicate that Weibull-GEV PDF is able to describe unimodal as well as bimodal wind distributions accurately whereas GEV-lognormal PDF is able to describe familiar bell-shaped unimodal distribution well. Results show that mixture probability functions are better alternatives to conventional Weibull, two-component mixture Weibull, gamma, and lognormal PDFs to describe wind speed characteristics.