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Importantly, this method can be generalized and applied to solve general sequential adaptive decision-making problems.
In this paper, we focus on a general sequential design with a general parametric model.
Subtypes of glutamatergic neurons (e.g., layer-specific fates in the cortex) are further determined by combinations of transcription factors, superimposed on general sequential programs.
The first part of the paper consists of a brief statement of the statistical concepts and procedures involved in obtaining precise estimates of parameters, the logic of the design criterion, and the general sequential design procedures.
In general, sequential designs require fewer patients, although they make the study more complex because there is a need for continuous information about the patients' status; this latter aspect requires greater effort in terms of both study monitoring and patient follow-up.
Q-learning is an algorithm for general sequential decision processes that converges towards the optimal policy, given certain conditions [3].
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In general, two sequential lavages are used.
In general, the sequential pathogenesis model of ARL suggests the following steps toward disease development: 1) HIV integration near a gene that promotes cellular proliferation; 2) macrophage proliferation; 3) production of lymphostimulatory products in oligoclonal macrophages leading to polyclonal proliferation of B-cells and monoclonal outgrowths; 4) tumorigenesis [3].
This analysis shows the advantages derived from the use of a general non-sequential optical software, proposing criteria and procedures in order to establish dedicated optical merit figures, which are suggested and evaluated from the point of view of their effectiveness to achieve a favorable layout design.
The general non-sequential MCS algorithm used for evaluating a CCS in this study is shown in Fig. 1.
The second contribution of the paper is to introduce a general class of sequential algorithms, denoted as Sequential Probabilistic Validation (SPV).
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