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10:35, 9 марта 2026Экономика
。关于这个话题，有道翻译下载提供了深入分析
过去，物理实验大多是“按步骤操作、验证已知结论”的重复性训练，学生很难体会到科学发现的乐趣。但在北邮睿析实验平台上，学生借助AI数据挖掘工具，不再是被动验证，而是主动“对话”数据——他们将传统研究中依靠直觉的“试错法”升级为“AI启发式探索”。这种虚实融通、沉浸感强、鼓励探索的新型实验范式，让本科生也能接触到前沿的“AI+物理”交叉研究方法，从而更好地培养“大物理观”。从被动接受到主动发现，从学会知识到学会探索，正是智能时代我们希望学生具备的能力。
If both the likelihood and the prior carry little information, then the posterior will be very uncertain. This is a perfect example where we can see how using a different prior, one which includes some knowledge about the problem, can help. Since nnn is an integer which is likely close to 4, I will use a geometric distribution as prior n∼3+Geom(q)n \sim 3 + \mathrm{Geom}(q)n∼3+Geom(q), with q=0.5q = 0.5q=0.5. In the piece of code below, I use pymc to do this numerically and I find nˉ=4.10\bar n = 4.10nˉ=4.10 with credible interval [4,5][4, 5][4,5]. While the interval is the same, what matters is that the distribution is edging closer to 4 (see the mean), showing our uncertainty is shrinking.
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