2026年2月第4周，40家上市公司曝光风险事件51起，风险指数110.70，其中治理风险占30.3%，环境风险占17.3%，社会风险占52.5%。中国神华（601088）、中国中铁（601390）、达华智能（002512）、云天化（600096）、龙洲股份（002682）ESG 风险级别达到 III 级。达华智能被ST，曾年报虚增利润超8500万元、龙洲股份旗下公司产品致近千辆纯电公交电池衰减停运等重大风险事件值得关注。
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The new MacBook Pros will be available in 14- and 16-inch sizes. The M5 Pro variants will start at $2,199 and $2,699, respectively, with discounts of up to $200 for education shoppers who buy direct. They come with 1TB of base storage. Apple recommends them "for users running complex workflows, like coders optimizing algorithms and photographers processing massive image libraries," according to a company press release.，推荐阅读体育直播获取更多信息
Consider a Bayesian agent attempting to discover a pattern in the world. Upon observing initial data d0d_{0}, they form a posterior distribution p(h|d0)p(h|d_{0}) and sample a hypothesis h∗h^{*} from this distribution. They then interact with a chatbot, sharing their belief h∗h^{*} in the hopes of obtaining further evidence. An unbiased chatbot would ignore h∗h^{*} and generate subsequent data from the true data-generating process, d1∼p(d|true process)d_{1}\sim p(d|\text{true process}). The Bayesian agent then updates their belief via p(h|d0,d1)∝p(d1|h)p(h|d0)p(h|d_{0},d_{1})\propto p(d_{1}|h)p(h|d_{0}). As this process continues, the Bayesian agent will get closer to the truth. After nn interactions, the beliefs of the agent are p(h|d0,…dn)∝p(h|d0)∏i=1np(di|h)p(h|d_{0},\ldots d_{n})\propto p(h|d_{0})\prod_{i=1}^{n}p(d_{i}|h) for di∼p(d|true process)d_{i}\sim p(d|\text{true process}). Taking the logarithm of the right hand side, this becomes logp(h|d0)+∑i=1nlogp(di|h)\log p(h|d_{0})+\sum_{i=1}^{n}\log p(d_{i}|h). Since the data did_{i} are drawn from p(d|true process)p(d|\text{true process}), ∑i=1nlogp(di|h)\sum_{i=1}^{n}\log p(d_{i}|h) is a Monte Carlo approximation of n∫dp(d|true process)logp(d|h)n\int_{d}p(d|\text{true process})\log p(d|h), which is nn times the negative cross-entropy of p(d|true process)p(d|\text{true process}) and p(d|h)p(d|h). As nn becomes large the sum of log likelihoods will approach this value, meaning that the Bayesian agent will favor the hypothesis that has lowest cross-entropy with the truth. If there is an hh that matches the true process, that minimizes the cross-entropy and p(h|d0,…,dn)p(h|d_{0},\ldots,d_{n}) will converge to 1 for that hypothesis and 0 for all other hypotheses.