The objective of this study was to develop a framework describing the milk production curve in sows as affected by parity, method of milk yield (MY) determination, litter size (LS), and litter gain (LG). A database containing data on LS, LG, dietary protein and fat content, MY, and composition measured on more than 1 d during lactation and method for determining MY from peer reviewed publications and individual sow data from 3 studies was constructed. A Bayesian hierarchical model was developed to analyze milk production data. The classical Wood curve was used to model time trends in MY during lactation, and it was re-parameterized expressing the natural logarithm of MY values at d 5, 20, and 30 as functional parameters. The model incorporated random effects of experiment, sow nested within experiment, and fixed effects of LS, LG, parity, and method through the functional parameters of the Wood curve. A second set of models were constructed to analyze milk composition data, including day in milk, LS, dietary protein, and fat contents. Four scenarios with different LG and LS were constructed using the framework to estimate the energy output in milk at different days during lactation. The estimated energy output was compared with energy output values calculated using the 1998 NRC method. Milk yield was underestimated by approximately 20% with the weigh-suckle-weigh technique compared with the deuterium oxide dilution technique (P < 0.001). The mean LG and LS for the dataset were 2.05 kg/d (1.0; 3.3) and 9.5 piglets (5; 14), respectively. The MY was affected by LS on d 5 and 20 (P < 0.001) and by LG on d 20 (P < 0.001) and d 30 (P = 0.004). The mean time to peak lactation was 18.7 d (SD = 1.06) postpartum and mean MY at peak lactation was 9.23 kg (SD = 0.14). The average protein, lactose, and fat content of milk was 5.22 (SD = 0.06), 5.41 (SD = 0.08), and 7.32% (SD = 0.17%), respectively. The NE requirement for lactation increased from d 5 to 20 because of increased MY. Requirements also increased with increasing LG and LS. The framework could be used to predict energy and protein requirements for lactation under different production expectations and can be incorporated into a whole animal model for determination of energy and nutrient requirements for lactating sows, which can optimize sow performance and longevity.