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Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
Mathematical Q models provide a model of the earth's response to seismic waves.In reflection seismology, the anelastic attenuation factor, often expressed as seismic quality factor or Q, which is inversely proportional to attenuation factor, quantifies the effects of anelastic attenuation on the seismic wavelet caused by fluid movement and grain boundary friction.
Quantitative structure–activity relationship models (QSAR models) are regression or classification models used in the chemical and biological sciences and engineering. Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable (Y), while classification QSAR models ...
Non-seasonal ARIMA models are usually denoted ARIMA(p, d, q) where parameters p, d, q are non-negative integers: p is the order (number of time lags) of the autoregressive model, d is the degree of differencing (the number of times the data have had past values subtracted), and q is the order of the moving-average model.
The model is usually denoted ARMA(p, q), where p is the order of AR and q is the order of MA. The general ARMA model was described in the 1951 thesis of Peter Whittle , Hypothesis testing in time series analysis , and it was popularized in the 1970 book by George E. P. Box and Gwilym Jenkins .
The EOQ model and its sister, the economic production quantity model (EPQ), have been criticised for "their restrictive set[s] of assumptions. [14] Guga and Musa make use of the model for an Albanian business case study and conclude that the model is "perfect theoretically, but not very suitable from the practical perspective of this firm". [15]
The acronyms "ARFIMA" or "FARIMA" are often used, although it is also conventional to simply extend the "ARIMA(p, d, q)" notation for models, by simply allowing the order of differencing, d, to take fractional values.
The (Q,r) model addresses the question of when and how much to order, aiming to minimize total inventory costs, which typically include ordering costs, holding costs, and shortage costs. It specifies that an order of size Q should be placed when the inventory level reaches a reorder point r. The (Q,r) model is widely applied in various ...