reduced transition probability

简明释义

约化跃迁几率

英英释义

例句

1.The simulation indicated that environmental changes led to a reduced transition probability 减少的转移概率 for species migration patterns.

模拟表明，环境变化导致物种迁徙模式的减少的转移概率 reduced transition probability。

2.The team's findings suggest that a reduced transition probability 减少的转移概率 in the disease spread model could indicate effective public health interventions.

团队的发现表明，在疾病传播模型中，减少的转移概率 reduced transition probability可能表明公共卫生干预措施有效。

3.By analyzing the data, researchers found that the reduced transition probability 减少的转移概率 in customer behavior was linked to increased competition.

通过分析数据，研究人员发现客户行为中的减少的转移概率 reduced transition probability与竞争加剧有关。

4.In a Markov model, we observed a significant decrease in the reduced transition probability 减少的转移概率 between states A and B after implementing new policies.

在一个马尔可夫模型中，我们观察到在实施新政策后，状态A和B之间的减少的转移概率 reduced transition probability显著下降。

5.In financial markets, a reduced transition probability 减少的转移概率 from high-risk to low-risk assets can signal investor confidence.

在金融市场中，从高风险资产到低风险资产的减少的转移概率 reduced transition probability可能暗示投资者信心。

作文

In the realm of probability theory and statistics, the concept of transition probability plays a crucial role in understanding dynamic systems. Transition probability refers to the likelihood of moving from one state to another within a probabilistic framework. However, when we discuss reduced transition probability, we are typically referring to a scenario where this likelihood is diminished due to certain constraints or changes in the system. This concept is particularly important in fields such as Markov processes, economics, and even machine learning, where understanding the flow between states can significantly impact predictions and outcomes.To illustrate this, consider a simple example of a weather system. If we define various states such as sunny, rainy, and cloudy, the transition probabilities would indicate how likely it is for the weather to change from one state to another. For instance, if it is currently sunny, there might be a high transition probability to remain sunny the next day, but a lower probability of transitioning to a rainy day. Now, if we introduce a factor such as an approaching storm, this could lead to a reduced transition probability of remaining sunny, as the likelihood of rain increases. This change fundamentally alters our predictions about the weather.In economic models, reduced transition probability can also play a vital role. For example, consider a market that is experiencing a downturn. The transition probabilities of consumers moving from one spending behavior to another may be reduced due to uncertainty and fear in the economy. When consumers are less confident, they may be less likely to make significant purchases, leading to a reduced transition probability of moving from saving to spending. This can create a feedback loop, wherein reduced spending leads to further economic decline, thus perpetuating the cycle of reduced transition probabilities across various economic behaviors.Moreover, in machine learning, particularly in reinforcement learning, reduced transition probability can affect how agents learn optimal policies. When an agent interacts with an environment, it relies on the transition probabilities to make decisions based on previous experiences. If certain actions result in a reduced transition probability of achieving desired outcomes, the agent may need to adapt its strategy accordingly. For instance, if an agent learns that taking a specific action often leads to failure, it will decrease the likelihood of choosing that action in the future, thus altering its learning trajectory.Understanding reduced transition probability is essential for making informed decisions in various domains. Whether in predicting weather patterns, analyzing economic trends, or training intelligent systems, recognizing how and why transition probabilities can be reduced allows for better planning and forecasting. It emphasizes the importance of external factors that can influence the dynamics of a system, ultimately leading to more robust models and strategies.In conclusion, the term reduced transition probability encapsulates a critical aspect of probability theory that has far-reaching implications across multiple disciplines. By comprehending how transition probabilities can be altered by internal and external influences, we can enhance our ability to predict and respond to changes in complex systems. This understanding not only enriches our theoretical knowledge but also equips us with practical tools to navigate the uncertainties of real-world scenarios.

在概率论和统计学的领域中，转移概率的概念在理解动态系统时起着至关重要的作用。转移概率指的是在概率框架内从一个状态移动到另一个状态的可能性。然而，当我们讨论reduced transition probability时，通常是指由于某些约束或系统变化而导致这种可能性降低的情况。这个概念在马尔可夫过程、经济学甚至机器学习等领域尤为重要，因为理解状态之间的流动可以显著影响预测和结果。为了说明这一点，考虑一个简单的天气系统示例。如果我们定义各种状态，如晴天、雨天和多云，转移概率将指示天气从一个状态变化到另一个状态的可能性。例如，如果当前是晴天，接下来保持晴天的转移概率可能很高，但转变为雨天的概率较低。现在，如果我们引入一个因素，比如即将来临的风暴，这可能导致保持晴天的reduced transition probability降低，因为下雨的可能性增加。这种变化从根本上改变了我们对天气的预测。在经济模型中，reduced transition probability也可以发挥重要作用。例如，考虑一个正在经历衰退的市场。由于经济的不确定性和恐惧，消费者从一种消费行为转变为另一种行为的转移概率可能会降低。当消费者信心不足时，他们可能不太可能进行重大购买，从而导致从储蓄到消费的reduced transition probability。这可能会形成一个反馈循环，其中减少的消费进一步导致经济衰退，从而在各种经济行为中延续reduced transition probability的循环。此外，在机器学习中，特别是在强化学习中，reduced transition probability可以影响智能体学习最佳策略。当一个智能体与环境互动时，它依赖于转移概率根据之前的经验做出决策。如果某些行动导致实现期望结果的reduced transition probability，智能体可能需要相应地调整其策略。例如，如果一个智能体发现采取特定行动常常导致失败，它将减少未来选择该行动的可能性，从而改变其学习轨迹。理解reduced transition probability对于在各个领域做出明智的决策至关重要。无论是在预测天气模式、分析经济趋势还是训练智能系统，认识到转移概率如何以及为何可能降低，使得更好的规划和预测成为可能。它强调了可以影响系统动态的外部因素的重要性，最终导致更强大的模型和策略。总之，术语reduced transition probability概括了概率论的一个关键方面，该方面在多个学科中具有深远的影响。通过理解转移概率如何受到内部和外部影响的改变，我们可以增强预测和应对复杂系统变化的能力。这种理解不仅丰富了我们的理论知识，还为我们提供了应对现实世界不确定性的实用工具。